Date: (Mon) May 30, 2016
Data: Source: Training: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv”
New: “https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv”
Time period:
Based on analysis utilizing <> techniques,
Summary of key steps & error improvement stats:
Use plot.ly for interactive plots ?
varImp for randomForest crashes in caret version:6.0.41 -> submit bug report
extensions toward multiclass classification are scheduled for the next release
rm(list = ls())
set.seed(12345)
options(stringsAsFactors = FALSE)
source("~/Dropbox/datascience/R/mycaret.R")
source("~/Dropbox/datascience/R/mydsutils.R")
## Loading required package: caret
## Loading required package: lattice
## Loading required package: ggplot2
source("~/Dropbox/datascience/R/mypetrinet.R")
source("~/Dropbox/datascience/R/myplclust.R")
source("~/Dropbox/datascience/R/myplot.R")
source("~/Dropbox/datascience/R/myscript.R")
source("~/Dropbox/datascience/R/mytm.R")
# Gather all package requirements here
suppressPackageStartupMessages(require(doMC))
glbCores <- 6 # of cores on machine - 2
registerDoMC(glbCores)
suppressPackageStartupMessages(require(caret))
require(plyr)
## Loading required package: plyr
require(dplyr)
## Loading required package: dplyr
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:plyr':
##
## arrange, count, desc, failwith, id, mutate, rename, summarise,
## summarize
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
require(knitr)
## Loading required package: knitr
require(stringr)
## Loading required package: stringr
#source("dbgcaret.R")
#packageVersion("snow")
#require(sos); findFn("cosine", maxPages=2, sortby="MaxScore")
# Analysis control global variables
# Inputs
# url/name = "<PathPointer>"; if url specifies a zip file, name = "<filename>";
# or named collection of <PathPointer>s
# sep = choose from c(NULL, "\t")
glbObsTrnFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/train2016.csv"
# or list(url = c(NULL, <.inp1> = "<path1>", <.inp2> = "<path2>"))
#, splitSpecs = list(method = "copy" # default when glbObsNewFile is NULL
# select from c("copy", NULL ???, "condition", "sample", )
# ,nRatio = 0.3 # > 0 && < 1 if method == "sample"
# ,seed = 123 # any integer or glbObsTrnPartitionSeed if method == "sample"
# ,condition = # or 'is.na(<var>)'; '<var> <condition_operator> <value>'
# )
)
glbObsNewFile <- list(url = "https://inclass.kaggle.com/c/can-we-predict-voting-outcomes/download/test2016.csv")
glbObsDropCondition <- NULL # : default
# enclose in single-quotes b/c condition might include double qoutes
# use | & ; NOT || &&
# '<condition>'
# 'grepl("^First Draft Video:", glbObsAll$Headline)'
# 'is.na(glbObsAll[, glb_rsp_var_raw])'
# '(is.na(glbObsAll[, glb_rsp_var_raw]) & grepl("Train", glbObsAll[, glbFeatsId]))'
# 'is.na(strptime(glbObsAll[, "Date"], glbFeatsDateTime[["Date"]]["format"], tz = glbFeatsDateTime[["Date"]]["timezone"]))'
#nrow(do.call("subset",list(glbObsAll, parse(text=paste0("!(", glbObsDropCondition, ")")))))
glb_obs_repartition_train_condition <- NULL # : default
# "<condition>"
glb_max_fitobs <- NULL # or any integer
glbObsTrnPartitionSeed <- 123 # or any integer
glb_is_regression <- FALSE; glb_is_classification <- !glb_is_regression;
glb_is_binomial <- TRUE # or TRUE or FALSE
glb_rsp_var_raw <- "Party"
# for classification, the response variable has to be a factor
glb_rsp_var <- "Party.fctr"
# if the response factor is based on numbers/logicals e.g (0/1 OR TRUE/FALSE vs. "A"/"B"),
# or contains spaces (e.g. "Not in Labor Force")
# caret predict(..., type="prob") crashes
glb_map_rsp_raw_to_var <- #NULL
function(raw) {
# return(raw ^ 0.5)
# return(log(raw))
# return(log(1 + raw))
# return(log10(raw))
# return(exp(-raw / 2))
ret_vals <- rep_len(NA, length(raw)); ret_vals[!is.na(raw)] <- ifelse(raw[!is.na(raw)] == "Republican", "R", "D"); return(relevel(as.factor(ret_vals), ref = "R"))
# as.factor(paste0("B", raw))
# as.factor(gsub(" ", "\\.", raw))
}
#if glb_rsp_var_raw is numeric:
#print(summary(glbObsAll[, glb_rsp_var_raw]))
#glb_map_rsp_raw_to_var(tst <- c(NA, as.numeric(summary(glbObsAll[, glb_rsp_var_raw]))))
#if glb_rsp_var_raw is character:
#print(table(glbObsAll[, glb_rsp_var_raw], useNA = "ifany"))
# print(table(glb_map_rsp_raw_to_var(tst <- glbObsAll[, glb_rsp_var_raw]), useNA = "ifany"))
glb_map_rsp_var_to_raw <- #NULL
function(var) {
# return(var ^ 2.0)
# return(exp(var))
# return(10 ^ var)
# return(-log(var) * 2)
# as.numeric(var)
# levels(var)[as.numeric(var)]
sapply(levels(var)[as.numeric(var)], function(elm)
if (is.na(elm)) return(elm) else
if (elm == 'R') return("Republican") else
if (elm == 'D') return("Democrat") else
stop("glb_map_rsp_var_to_raw: unexpected value: ", elm)
)
# gsub("\\.", " ", levels(var)[as.numeric(var)])
# c("<=50K", " >50K")[as.numeric(var)]
# c(FALSE, TRUE)[as.numeric(var)]
}
# print(table(glb_map_rsp_var_to_raw(glb_map_rsp_raw_to_var(tst)), useNA = "ifany"))
if ((glb_rsp_var != glb_rsp_var_raw) && is.null(glb_map_rsp_raw_to_var))
stop("glb_map_rsp_raw_to_var function expected")
# List info gathered for various columns
# <col_name>: <description>; <notes>
# USER_ID - an anonymous id unique to a given user
# YOB - the year of birth of the user
# Gender - the gender of the user, either Male or Female
# Income - the household income of the user. Either not provided, or one of "under $25,000", "$25,001 - $50,000", "$50,000 - $74,999", "$75,000 - $100,000", "$100,001 - $150,000", or "over $150,000".
# HouseholdStatus - the household status of the user. Either not provided, or one of "Domestic Partners (no kids)", "Domestic Partners (w/kids)", "Married (no kids)", "Married (w/kids)", "Single (no kids)", or "Single (w/kids)".
# EducationalLevel - the education level of the user. Either not provided, or one of "Current K-12", "High School Diploma", "Current Undergraduate", "Associate's Degree", "Bachelor's Degree", "Master's Degree", or "Doctoral Degree".
# Party - the political party for whom the user intends to vote for. Either "Democrat" or "Republican
# Q124742, Q124122, . . . , Q96024 - 101 different questions that the users were asked on Show of Hands. If the user didn't answer the question, there is a blank. For information about the question text and possible answers, see the file Questions.pdf.
# currently does not handle more than 1 column; consider concatenating multiple columns
# If glbFeatsId == NULL, ".rownames <- as.numeric(row.names())" is the default
glbFeatsId <- "USER_ID" # choose from c(NULL : default, "<id_feat>")
glbFeatsCategory <- "Hhold.fctr" # choose from c(NULL : default, "<category_feat>")
# glbFeatsCategory <- "Q109244.fctr" # choose from c(NULL : default, "<category_feat>") -> OOB performed worse than "Hhold.fctr"
# User-specified exclusions
glbFeatsExclude <- c(NULL
# Feats that shd be excluded due to known causation by prediction variable
# , "<feat1", "<feat2>"
# Feats that are factors with unique values (as % of nObs) > 49 (empirically derived)
# Feats that are linear combinations (alias in glm)
# Feature-engineering phase -> start by excluding all features except id & category &
# work each one in
, "USER_ID", "YOB", "Gender", "Income", "HouseholdStatus", "EducationLevel"
,"Q124742","Q124122"
,"Q123621","Q123464"
,"Q122771","Q122770","Q122769","Q122120"
,"Q121700","Q121699","Q121011"
,"Q120978","Q120650","Q120472","Q120379","Q120194","Q120014","Q120012"
,"Q119851","Q119650","Q119334"
,"Q118892","Q118237","Q118233","Q118232","Q118117"
,"Q117193","Q117186"
,"Q116797","Q116881","Q116953","Q116601","Q116441","Q116448","Q116197"
,"Q115602","Q115777","Q115610","Q115611","Q115899","Q115390","Q115195"
,"Q114961","Q114748","Q114517","Q114386","Q114152"
,"Q113992","Q113583","Q113584","Q113181"
,"Q112478","Q112512","Q112270"
,"Q111848","Q111580","Q111220"
,"Q110740"
,"Q109367","Q109244"
,"Q108950","Q108855","Q108617","Q108856","Q108754","Q108342","Q108343"
,"Q107869","Q107491"
,"Q106993","Q106997","Q106272","Q106388","Q106389","Q106042"
,"Q105840","Q105655"
,"Q104996"
,"Q103293"
,"Q102906","Q102674","Q102687","Q102289","Q102089"
,"Q101162","Q101163","Q101596"
,"Q100689","Q100680","Q100562","Q100010"
,"Q99982"
,"Q99716"
,"Q99581"
,"Q99480"
,"Q98869"
,"Q98578"
,"Q98197"
,"Q98059","Q98078"
,"Q96024" # Done
,".pos")
if (glb_rsp_var_raw != glb_rsp_var)
glbFeatsExclude <- union(glbFeatsExclude, glb_rsp_var_raw)
glbFeatsInteractionOnly <- list()
#glbFeatsInteractionOnly[["<child_feat>"]] <- "<parent_feat>"
glbFeatsDrop <- c(NULL
# , "<feat1>", "<feat2>"
)
glb_map_vars <- NULL # or c("<var1>", "<var2>")
glb_map_urls <- list();
# glb_map_urls[["<var1>"]] <- "<var1.url>"
# Derived features; Use this mechanism to cleanse data ??? Cons: Data duplication ???
glbFeatsDerive <- list();
# glbFeatsDerive[["<feat.my.sfx>"]] <- list(
# mapfn = function(<arg1>, <arg2>) { return(function(<arg1>, <arg2>)) }
# , args = c("<arg1>", "<arg2>"))
#myprint_df(data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos)))
#data.frame(ImageId = mapfn(glbObsAll$.src, glbObsAll$.pos))[7045:7055, ]
# character
# mapfn = function(Education) { raw <- Education; raw[is.na(raw)] <- "NA.my"; return(as.factor(raw)) }
# mapfn = function(Week) { return(substr(Week, 1, 10)) }
# mapfn = function(Name) { return(sapply(Name, function(thsName)
# str_sub(unlist(str_split(thsName, ","))[1], 1, 1))) }
# mapfn = function(descriptor) { return(plyr::revalue(descriptor, c(
# "ABANDONED BUILDING" = "OTHER",
# "**" = "**"
# ))) }
# mapfn = function(description) { mod_raw <- description;
# This is here because it does not work if it's in txt_map_filename
# mod_raw <- gsub(paste0(c("\n", "\211", "\235", "\317", "\333"), collapse = "|"), " ", mod_raw)
# Don't parse for "." because of ".com"; use customized gsub for that text
# mod_raw <- gsub("(\\w)(!|\\*|,|-|/)(\\w)", "\\1\\2 \\3", mod_raw);
# Some state acrnoyms need context for separation e.g.
# LA/L.A. could either be "Louisiana" or "LosAngeles"
# modRaw <- gsub("\\bL\\.A\\.( |,|')", "LosAngeles\\1", modRaw);
# OK/O.K. could either be "Oklahoma" or "Okay"
# modRaw <- gsub("\\bACA OK\\b", "ACA OKay", modRaw);
# modRaw <- gsub("\\bNow O\\.K\\.\\b", "Now OKay", modRaw);
# PR/P.R. could either be "PuertoRico" or "Public Relations"
# modRaw <- gsub("\\bP\\.R\\. Campaign", "PublicRelations Campaign", modRaw);
# VA/V.A. could either be "Virginia" or "VeteransAdministration"
# modRaw <- gsub("\\bthe V\\.A\\.\\:", "the VeteranAffairs:", modRaw);
#
# Custom mods
# return(mod_raw) }
# numeric
# Create feature based on record position/id in data
glbFeatsDerive[[".pos"]] <- list(
mapfn = function(raw1) { return(1:length(raw1)) }
, args = c(".rnorm"))
# glbFeatsDerive[[".pos.y"]] <- list(
# mapfn = function(raw1) { return(1:length(raw1)) }
# , args = c(".rnorm"))
# Add logs of numerics that are not distributed normally
# Derive & keep multiple transformations of the same feature, if normality is hard to achieve with just one transformation
# Right skew: logp1; sqrt; ^ 1/3; logp1(logp1); log10; exp(-<feat>/constant)
# glbFeatsDerive[["WordCount.log1p"]] <- list(
# mapfn = function(WordCount) { return(log1p(WordCount)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.root2"]] <- list(
# mapfn = function(WordCount) { return(WordCount ^ (1/2)) }
# , args = c("WordCount"))
# glbFeatsDerive[["WordCount.nexp"]] <- list(
# mapfn = function(WordCount) { return(exp(-WordCount)) }
# , args = c("WordCount"))
#print(summary(glbObsAll$WordCount))
#print(summary(mapfn(glbObsAll$WordCount)))
# If imputation shd be skipped for this feature
# glbFeatsDerive[["District.fctr"]] <- list(
# mapfn = function(District) {
# raw <- District;
# ret_vals <- rep_len("NA", length(raw));
# ret_vals[!is.na(raw)] <- sapply(raw[!is.na(raw)], function(elm)
# ifelse(elm < 10, "1-9",
# ifelse(elm < 20, "10-19", "20+")));
# return(relevel(as.factor(ret_vals), ref = "NA"))
# }
# , args = c("District"))
# YOB options:
# 1. Missing data:
# 1.1 0 -> Does not improve baseline
# 1.2 Cut factors & "NA" is a level
# 2. Data corrections: < 1928 & > 2000
# 3. Scale YOB
# 4. Add Age
glbFeatsDerive[["YOB.Age.fctr"]] <- list(
mapfn = function(raw1) {
raw <- 2016 - raw1
# raw[!is.na(raw) & raw >= 2010] <- NA
raw[!is.na(raw) & (raw <= 15)] <- NA
raw[!is.na(raw) & (raw >= 90)] <- NA
retVal <- rep_len("NA", length(raw))
# breaks = c(1879, seq(1949, 1989, 10), 2049)
# cutVal <- cut(raw[!is.na(raw)], breaks = breaks,
# labels = as.character(breaks + 1)[1:(length(breaks) - 1)])
cutVal <- cut(raw[!is.na(raw)], breaks = c(15, 20, 25, 30, 35, 40, 50, 65, 90))
retVal[!is.na(raw)] <- levels(cutVal)[cutVal]
return(factor(retVal, levels = c("NA"
,"(15,20]","(20,25]","(25,30]","(30,35]","(35,40]","(40,50]","(50,65]","(65,90]"),
ordered = TRUE))
}
, args = c("YOB"))
glbFeatsDerive[["Gender.fctr"]] <- list(
mapfn = function(raw1) {
raw <- raw1
raw[raw %in% ""] <- "N"
raw <- gsub("Male" , "M", raw, fixed = TRUE)
raw <- gsub("Female", "F", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("Gender"))
glbFeatsDerive[["Income.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("under $25,000" , "<25K" , raw, fixed = TRUE)
raw <- gsub("$25,001 - $50,000" , "25-50K" , raw, fixed = TRUE)
raw <- gsub("$50,000 - $74,999" , "50-75K" , raw, fixed = TRUE)
raw <- gsub("$75,000 - $100,000" , "75-100K" , raw, fixed = TRUE)
raw <- gsub("$100,001 - $150,000", "100-150K", raw, fixed = TRUE)
raw <- gsub("over $150,000" , ">150K" , raw, fixed = TRUE)
return(factor(raw, levels = c("N","<25K","25-50K","50-75K","75-100K","100-150K",">150K"),
ordered = TRUE))
}
, args = c("Income"))
glbFeatsDerive[["Hhold.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Domestic Partners (no kids)", "PKn", raw, fixed = TRUE)
raw <- gsub("Domestic Partners (w/kids)" , "PKy", raw, fixed = TRUE)
raw <- gsub("Married (no kids)" , "MKn", raw, fixed = TRUE)
raw <- gsub("Married (w/kids)" , "MKy", raw, fixed = TRUE)
raw <- gsub("Single (no kids)" , "SKn", raw, fixed = TRUE)
raw <- gsub("Single (w/kids)" , "SKy", raw, fixed = TRUE)
return(relevel(as.factor(raw), ref = "N"))
}
, args = c("HouseholdStatus"))
glbFeatsDerive[["Edn.fctr"]] <- list(
mapfn = function(raw1) { raw <- raw1;
raw[raw %in% ""] <- "N"
raw <- gsub("Current K-12" , "K12", raw, fixed = TRUE)
raw <- gsub("High School Diploma" , "HSD", raw, fixed = TRUE)
raw <- gsub("Current Undergraduate", "CCg", raw, fixed = TRUE)
raw <- gsub("Associate's Degree" , "Ast", raw, fixed = TRUE)
raw <- gsub("Bachelor's Degree" , "Bcr", raw, fixed = TRUE)
raw <- gsub("Master's Degree" , "Msr", raw, fixed = TRUE)
raw <- gsub("Doctoral Degree" , "PhD", raw, fixed = TRUE)
return(factor(raw, levels = c("N","K12","HSD","CCg","Ast","Bcr","Msr","PhD"),
ordered = TRUE))
}
, args = c("EducationLevel"))
# for (qsn in c("Q124742","Q124122"))
# for (qsn in grep("Q12(.{4})(?!\\.fctr)", names(glbObsTrn), value = TRUE, perl = TRUE))
for (qsn in grep("Q", glbFeatsExclude, fixed = TRUE, value = TRUE))
glbFeatsDerive[[paste0(qsn, ".fctr")]] <- list(
mapfn = function(raw1) {
raw1[raw1 %in% ""] <- "NA"
rawVal <- unique(raw1)
if (length(setdiff(rawVal, (expVal <- c("NA", "No", "Ys")))) == 0) {
raw1 <- gsub("Yes", "Ys", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Me", "Circumstances")))) == 0) {
raw1 <- gsub("Circumstances", "Cs", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Grrr people", "Yay people!")))) == 0) {
raw1 <- gsub("Grrr people", "Gr", raw1, fixed = TRUE)
raw1 <- gsub("Yay people!", "Yy", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Idealist", "Pragmatist")))) == 0) {
raw1 <- gsub("Idealist" , "Id", raw1, fixed = TRUE)
raw1 <- gsub("Pragmatist", "Pr", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
} else
if (length(setdiff(rawVal, (expVal <- c("NA", "Private", "Public")))) == 0) {
raw1 <- gsub("Private", "Pt", raw1, fixed = TRUE)
raw1 <- gsub("Public" , "Pc", raw1, fixed = TRUE)
if (length(setdiff(rawVal, expVal)) > 0)
stop(qsn, " vals: ", paste0(rawVal, collapse = "|"),
" does not match expectation: ", paste0(expVal, collapse = "|"))
}
return(relevel(as.factor(raw1), ref = "NA"))
}
, args = c(qsn))
# If imputation of missing data is not working ...
# glbFeatsDerive[["FertilityRate.nonNA"]] <- list(
# mapfn = function(FertilityRate, Region) {
# RegionMdn <- tapply(FertilityRate, Region, FUN = median, na.rm = TRUE)
#
# retVal <- FertilityRate
# retVal[is.na(FertilityRate)] <- RegionMdn[Region[is.na(FertilityRate)]]
# return(retVal)
# }
# , args = c("FertilityRate", "Region"))
# mapfn = function(HOSPI.COST) { return(cut(HOSPI.COST, 5, breaks = c(0, 100000, 200000, 300000, 900000), labels = NULL)) }
# mapfn = function(Rasmussen) { return(ifelse(sign(Rasmussen) >= 0, 1, 0)) }
# mapfn = function(startprice) { return(startprice ^ (1/2)) }
# mapfn = function(startprice) { return(log(startprice)) }
# mapfn = function(startprice) { return(exp(-startprice / 20)) }
# mapfn = function(startprice) { return(scale(log(startprice))) }
# mapfn = function(startprice) { return(sign(sprice.predict.diff) * (abs(sprice.predict.diff) ^ (1/10))) }
# factor
# mapfn = function(PropR) { return(as.factor(ifelse(PropR >= 0.5, "Y", "N"))) }
# mapfn = function(productline, description) { as.factor(gsub(" ", "", productline)) }
# mapfn = function(purpose) { return(relevel(as.factor(purpose), ref="all_other")) }
# mapfn = function(raw) { tfr_raw <- as.character(cut(raw, 5));
# tfr_raw[is.na(tfr_raw)] <- "NA.my";
# return(as.factor(tfr_raw)) }
# mapfn = function(startprice.log10) { return(cut(startprice.log10, 3)) }
# mapfn = function(startprice.log10) { return(cut(sprice.predict.diff, c(-1000, -100, -10, -1, 0, 1, 10, 100, 1000))) }
# , args = c("<arg1>"))
# multiple args
# mapfn = function(id, date) { return(paste(as.character(id), as.character(date), sep = "#")) }
# mapfn = function(PTS, oppPTS) { return(PTS - oppPTS) }
# mapfn = function(startprice.log10.predict, startprice) {
# return(spdiff <- (10 ^ startprice.log10.predict) - startprice) }
# mapfn = function(productline, description) { as.factor(
# paste(gsub(" ", "", productline), as.numeric(nchar(description) > 0), sep = "*")) }
# mapfn = function(.src, .pos) {
# return(paste(.src, sprintf("%04d",
# ifelse(.src == "Train", .pos, .pos - 7049)
# ), sep = "#")) }
# # If glbObsAll is not sorted in the desired manner
# mapfn=function(Week) { return(coredata(lag(zoo(orderBy(~Week, glbObsAll)$ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI) { return(coredata(lag(zoo(ILI), -2, na.pad=TRUE))) }
# mapfn=function(ILI.2.lag) { return(log(ILI.2.lag)) }
# glbFeatsDerive[["<var1>"]] <- glbFeatsDerive[["<var2>"]]
# tst <- "descr.my"; args_lst <- NULL; for (arg in glbFeatsDerive[[tst]]$args) args_lst[[arg]] <- glbObsAll[, arg]; print(head(args_lst[[arg]])); print(head(drv_vals <- do.call(glbFeatsDerive[[tst]]$mapfn, args_lst)));
# print(which_ix <- which(args_lst[[arg]] == 0.75)); print(drv_vals[which_ix]);
glbFeatsDateTime <- list()
# Use OlsonNames() to enumerate supported time zones
# glbFeatsDateTime[["<DateTimeFeat>"]] <-
# c(format = "%Y-%m-%d %H:%M:%S" or "%m/%e/%y", timezone = "US/Eastern", impute.na = TRUE,
# last.ctg = FALSE, poly.ctg = FALSE)
glbFeatsPrice <- NULL # or c("<price_var>")
glbFeatsImage <- list() #list(<imageFeat> = list(patchSize = 10)) # if patchSize not specified, no patch computation
glbFeatsText <- list()
Sys.setlocale("LC_ALL", "C") # For english
## [1] "C/C/C/C/C/en_US.UTF-8"
#glbFeatsText[["<TextFeature>"]] <- list(NULL,
# ,names = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-screened-names>
# ))))
# ,rareWords = myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL,
# <comma-separated-nonSCOWL-words>
# ))))
#)
# Text Processing Step: custom modifications not present in txt_munge -> use glbFeatsDerive
# Text Processing Step: universal modifications
glb_txt_munge_filenames_pfx <- "<projectId>_mytxt_"
# Text Processing Step: tolower
# Text Processing Step: myreplacePunctuation
# Text Processing Step: removeWords
glb_txt_stop_words <- list()
# Remember to use unstemmed words
if (length(glbFeatsText) > 0) {
require(tm)
require(stringr)
glb_txt_stop_words[["<txt_var>"]] <- sort(myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# Remove any words from stopwords
# , setdiff(myreplacePunctuation(stopwords("english")), c("<keep_wrd1>", <keep_wrd2>"))
# Remove salutations
,"mr","mrs","dr","Rev"
# Remove misc
#,"th" # Happy [[:digit::]]+th birthday
# Remove terms present in Trn only or New only; search for "Partition post-stem"
# ,<comma-separated-terms>
# cor.y.train == NA
# ,unlist(strsplit(paste(c(NULL
# ,"<comma-separated-terms>"
# ), collapse=",")
# freq == 1; keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# chisq.pval high (e.g. == 1); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
# nzv.freqRatio high (e.g. >= glbFeatsNzvFreqMax); keep c("<comma-separated-terms-to-keep>")
# ,<comma-separated-terms>
)))))
}
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^man", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 4866] > 0, c(glb_rsp_var, txtFeat)]
# To identify terms with a specific freq
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], freq == 1)$term), collapse = ",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], freq <= 2)$term), collapse = ",")
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% c("zinger"))
# To identify terms with a specific freq &
# are not stemmed together later OR is value of color.fctr (e.g. gold)
#paste0(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], (freq == 1) & !(term %in% c("blacked","blemish","blocked","blocks","buying","cables","careful","carefully","changed","changing","chargers","cleanly","cleared","connect","connects","connected","contains","cosmetics","default","defaulting","defective","definitely","describe","described","devices","displays","drop","drops","engravement","excellant","excellently","feels","fix","flawlessly","frame","framing","gentle","gold","guarantee","guarantees","handled","handling","having","install","iphone","iphones","keeped","keeps","known","lights","line","lining","liquid","liquidation","looking","lots","manuals","manufacture","minis","most","mostly","network","networks","noted","opening","operated","performance","performs","person","personalized","photograph","physically","placed","places","powering","pre","previously","products","protection","purchasing","returned","rotate","rotation","running","sales","second","seconds","shipped","shuts","sides","skin","skinned","sticker","storing","thats","theres","touching","unusable","update","updates","upgrade","weeks","wrapped","verified","verify") ))$term), collapse = ",")
#print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (freq <= 2)))
#glbObsAll[which(terms_mtrx[, 229] > 0), glbFeatsText]
# To identify terms with cor.y == NA
#orderBy(~-freq+term, subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
#paste(sort(subset(glb_post_stop_words_terms_df_lst[[txtFeat]], is.na(cor.y))[, "term"]), collapse=",")
#orderBy(~-freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], is.na(cor.y)))
# To identify terms with low cor.y.abs
#head(orderBy(~cor.y.abs+freq+term, subset(glb_post_stem_words_terms_df_lst[[txtFeat]], !is.na(cor.y))), 5)
# To identify terms with high chisq.pval
#subset(glb_post_stem_words_terms_df_lst[[txtFeat]], chisq.pval > 0.99)
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.99) & (freq <= 10))$term), collapse=",")
#paste0(sort(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (chisq.pval > 0.9))$term), collapse=",")
#head(orderBy(~-chisq.pval+freq+term, glb_post_stem_words_terms_df_lst[[txtFeat]]), 5)
#glbObsAll[glb_post_stem_words_terms_mtrx_lst[[txtFeat]][, 68] > 0, glbFeatsText]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^m", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
# To identify terms with high nzv.freqRatio
#summary(glb_post_stem_words_terms_df_lst[[txtFeat]]$nzv.freqRatio)
#paste0(sort(setdiff(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], (nzv.freqRatio >= glbFeatsNzvFreqMax) & (freq < 10) & (chisq.pval >= 0.05))$term, c( "128gb","3g","4g","gold","ipad1","ipad3","ipad4","ipadair2","ipadmini2","manufactur","spacegray","sprint","tmobil","verizon","wifion"))), collapse=",")
# To identify obs with a txt term
#tail(orderBy(~-freq+term, glb_post_stop_words_terms_df_lst[[txtFeat]]), 20)
#mydspObs(list(descr.my.contains="non"), cols=c("color", "carrier", "cellular", "storage"))
#grep("ever", dimnames(terms_stop_mtrx)$Terms)
#which(terms_stop_mtrx[, grep("ipad", dimnames(terms_stop_mtrx)$Terms)] > 0)
#glbObsAll[which(terms_stop_mtrx[, grep("16", dimnames(terms_stop_mtrx)$Terms)[1]] > 0), c(glbFeatsCategory, "storage", txtFeat)]
# Text Processing Step: screen for names # Move to glbFeatsText specs section in order of text processing steps
# glbFeatsText[["<txtFeat>"]]$names <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Person names for names screening
# ,<comma-separated-list>
#
# # Company names
# ,<comma-separated-list>
#
# # Product names
# ,<comma-separated-list>
# ))))
# glbFeatsText[["<txtFeat>"]]$rareWords <- myreplacePunctuation(str_to_lower(gsub(" ", "", c(NULL
# # Words not in SCOWL db
# ,<comma-separated-list>
# ))))
# To identify char vectors post glbFeatsTextMap
#grep("six(.*)hour", glb_txt_chr_lst[[txtFeat]], ignore.case = TRUE, value = TRUE)
#grep("[S|s]ix(.*)[H|h]our", glb_txt_chr_lst[[txtFeat]], value = TRUE)
# To identify whether terms shd be synonyms
#orderBy(~term, glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^moder", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ])
# term_row_df <- glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^came$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#
# cor(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][glbObsAll$.lcn == "Fit", term_row_df$pos], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# To identify which stopped words are "close" to a txt term
#sort(glbFeatsCluster)
# Text Processing Step: stemDocument
# To identify stemmed txt terms
#glb_post_stop_words_terms_df_lst[[txtFeat]][grep("^la$", glb_post_stop_words_terms_df_lst[[txtFeat]]$term), ]
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^con", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
#glbObsAll[which(terms_stem_mtrx[, grep("use", dimnames(terms_stem_mtrx)$Terms)[[1]]] > 0), c(glbFeatsId, "productline", txtFeat)]
#glbObsAll[which(TfIdf_stem_mtrx[, 191] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#glbObsAll[which(glb_post_stop_words_terms_mtrx_lst[[txtFeat]][, 6165] > 0), c(glbFeatsId, glbFeatsCategory, txtFeat)]
#which(glbObsAll$UniqueID %in% c(11915, 11926, 12198))
# Text Processing Step: mycombineSynonyms
# To identify which terms are associated with not -> combine "could not" & "couldn't"
#findAssocs(glb_full_DTM_lst[[txtFeat]], "not", 0.05)
# To identify which synonyms should be combined
#orderBy(~term, glb_post_stem_words_terms_df_lst[[txtFeat]][grep("^c", glb_post_stem_words_terms_df_lst[[txtFeat]]$term), ])
chk_comb_cor <- function(syn_lst) {
# cor(terms_stem_mtrx[glbObsAll$.src == "Train", grep("^(damag|dent|ding)$", dimnames(terms_stem_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
print(subset(glb_post_stem_words_terms_df_lst[[txtFeat]], term %in% syn_lst$syns))
print(subset(get_corpus_terms(tm_map(glbFeatsTextCorpus[[txtFeat]], mycombineSynonyms, list(syn_lst), lazy=FALSE)), term == syn_lst$word))
# cor(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])], glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
# cor(rowSums(terms_stop_mtrx[glbObsAll$.src == "Train", grep("^(damage|dent|ding)$", dimnames(terms_stop_mtrx)[[2]])]), glbObsTrn[, glb_rsp_var], use="pairwise.complete.obs")
}
#chk_comb_cor(syn_lst=list(word="cabl", syns=c("cabl", "cord")))
#chk_comb_cor(syn_lst=list(word="damag", syns=c("damag", "dent", "ding")))
#chk_comb_cor(syn_lst=list(word="dent", syns=c("dent", "ding")))
#chk_comb_cor(syn_lst=list(word="use", syns=c("use", "usag")))
glbFeatsTextSynonyms <- list()
# list parsed to collect glbFeatsText[[<txtFeat>]]$vldTerms
# glbFeatsTextSynonyms[["Hdln.my"]] <- list(NULL
# # people in places
# , list(word = "australia", syns = c("australia", "australian"))
# , list(word = "italy", syns = c("italy", "Italian"))
# , list(word = "newyork", syns = c("newyork", "newyorker"))
# , list(word = "Pakistan", syns = c("Pakistan", "Pakistani"))
# , list(word = "peru", syns = c("peru", "peruvian"))
# , list(word = "qatar", syns = c("qatar", "qatari"))
# , list(word = "scotland", syns = c("scotland", "scotish"))
# , list(word = "Shanghai", syns = c("Shanghai", "Shanzhai"))
# , list(word = "venezuela", syns = c("venezuela", "venezuelan"))
#
# # companies - needs to be data dependent
# # - e.g. ensure BNP in this experiment/feat always refers to BNPParibas
#
# # general synonyms
# , list(word = "Create", syns = c("Create","Creator"))
# , list(word = "cute", syns = c("cute","cutest"))
# , list(word = "Disappear", syns = c("Disappear","Fadeout"))
# , list(word = "teach", syns = c("teach", "taught"))
# , list(word = "theater", syns = c("theater", "theatre", "theatres"))
# , list(word = "understand", syns = c("understand", "understood"))
# , list(word = "weak", syns = c("weak", "weaken", "weaker", "weakest"))
# , list(word = "wealth", syns = c("wealth", "wealthi"))
#
# # custom synonyms (phrases)
#
# # custom synonyms (names)
# )
#glbFeatsTextSynonyms[["<txtFeat>"]] <- list(NULL
# , list(word="<stem1>", syns=c("<stem1>", "<stem1_2>"))
# )
for (txtFeat in names(glbFeatsTextSynonyms))
for (entryIx in 1:length(glbFeatsTextSynonyms[[txtFeat]])) {
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$word)
glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns <-
str_to_lower(glbFeatsTextSynonyms[[txtFeat]][[entryIx]]$syns)
}
glbFeatsTextSeed <- 181
# tm options include: check tm::weightSMART
glb_txt_terms_control <- list( # Gather model performance & run-time stats
# weighting = function(x) weightSMART(x, spec = "nnn")
# weighting = function(x) weightSMART(x, spec = "lnn")
# weighting = function(x) weightSMART(x, spec = "ann")
# weighting = function(x) weightSMART(x, spec = "bnn")
# weighting = function(x) weightSMART(x, spec = "Lnn")
#
weighting = function(x) weightSMART(x, spec = "ltn") # default
# weighting = function(x) weightSMART(x, spec = "lpn")
#
# weighting = function(x) weightSMART(x, spec = "ltc")
#
# weighting = weightBin
# weighting = weightTf
# weighting = weightTfIdf # : default
# termFreq selection criteria across obs: tm default: list(global=c(1, Inf))
, bounds = list(global = c(1, Inf))
# wordLengths selection criteria: tm default: c(3, Inf)
, wordLengths = c(1, Inf)
)
glb_txt_cor_var <- glb_rsp_var # : default # or c(<feat>)
# select one from c("union.top.val.cor", "top.cor", "top.val", default: "top.chisq", "sparse")
glbFeatsTextFilter <- "top.chisq"
glbFeatsTextTermsMax <- rep(10, length(glbFeatsText)) # :default
names(glbFeatsTextTermsMax) <- names(glbFeatsText)
# Text Processing Step: extractAssoc
glbFeatsTextAssocCor <- rep(1, length(glbFeatsText)) # :default
names(glbFeatsTextAssocCor) <- names(glbFeatsText)
# Remember to use stemmed terms
glb_important_terms <- list()
# Text Processing Step: extractPatterns (ngrams)
glbFeatsTextPatterns <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- list()
#glbFeatsTextPatterns[[<txtFeat>>]] <- c(metropolitan.diary.colon = "Metropolitan Diary:")
# Have to set it even if it is not used
# Properties:
# numrows(glb_feats_df) << numrows(glbObsFit
# Select terms that appear in at least 0.2 * O(FP/FN(glbObsOOB)) ???
# numrows(glbObsOOB) = 1.1 * numrows(glbObsNew) ???
glb_sprs_thresholds <- NULL # or c(<txtFeat1> = 0.988, <txtFeat2> = 0.970, <txtFeat3> = 0.970)
glbFctrMaxUniqVals <- 20 # default: 20
glb_impute_na_data <- FALSE # or TRUE
glb_mice_complete.seed <- 144 # or any integer
glbFeatsCluster <- c("YOB.Age.fctr", "Gender.fctr", "Income.fctr",
# "Hhold.fctr",
"Edn.fctr",
paste(grep("^Q.", glbFeatsExclude, value = TRUE), "fctr", sep = ".")) # NULL : default or c("<feat1>", "<feat2>")
# glbFeatsCluster <- grep(paste0("[",
# toupper(paste0(substr(glbFeatsText, 1, 1), collapse = "")),
# "]\\.[PT]\\."),
# names(glbObsAll), value = TRUE)
glb_cluster.seed <- 189 # or any integer
glbClusterEntropyVar <- NULL # c(glb_rsp_var, as.factor(cut(glb_rsp_var, 3)), default: NULL)
glbFeatsClusterVarsExclude <- TRUE # default FALSE
glb_interaction_only_feats <- NULL # : default or c(<parent_feat> = "<child_feat>")
glbFeatsNzvFreqMax <- 19 # 19 : caret default
glbFeatsNzvUniqMin <- 10 # 10 : caret default
glbRFESizes <- list()
#glbRFESizes[["mdlFamily"]] <- c(4, 8, 16, 32, 64, 67, 68, 69) # Accuracy@69/70 = 0.8258
glbObsFitOutliers <- list()
# If outliers.n >= 10; consider concatenation of interaction vars
# glbObsFitOutliers[["<mdlFamily>"]] <- c(NULL
# is.na(.rstudent)
# max(.rstudent)
# is.na(.dffits)
# .hatvalues >= 0.99
# -38,167,642 < minmax(.rstudent) < 49,649,823
# , <comma-separated-<glbFeatsId>>
# )
glbObsTrnOutliers <- list()
glbObsTrnOutliers[["Final"]] <- union(glbObsFitOutliers[["All.X"]],
c(NULL
))
# influence.measures: car::outlier; rstudent; dffits; hatvalues; dfbeta; dfbetas
#mdlId <- "All.X##rcv#glm"; obs_df <- fitobs_df
#mdlId <- "RFE.X.glm"; obs_df <- fitobs_df
#mdlId <- "Final.glm"; obs_df <- trnobs_df
#mdlId <- "CSM2.X.glm"; obs_df <- fitobs_df
#print(outliers <- car::outlierTest(glb_models_lst[[mdlId]]$finalModel))
#mdlIdFamily <- paste0(head(unlist(str_split(mdlId, "\\.")), -1), collapse="."); obs_df <- dplyr::filter_(obs_df, interp(~(!(var %in% glbObsFitOutliers[[mdlIdFamily]])), var = as.name(glbFeatsId))); model_diags_df <- cbind(obs_df, data.frame(.rstudent=stats::rstudent(glb_models_lst[[mdlId]]$finalModel)), data.frame(.dffits=stats::dffits(glb_models_lst[[mdlId]]$finalModel)), data.frame(.hatvalues=stats::hatvalues(glb_models_lst[[mdlId]]$finalModel)));print(summary(model_diags_df[, c(".rstudent",".dffits",".hatvalues")])); table(cut(model_diags_df$.hatvalues, breaks=c(0.00, 0.98, 0.99, 1.00)))
#print(subset(model_diags_df, is.na(.rstudent))[, glbFeatsId])
#print(model_diags_df[which.max(model_diags_df$.rstudent), ])
#print(subset(model_diags_df, is.na(.dffits))[, glbFeatsId])
#print(model_diags_df[which.min(model_diags_df$.dffits), ])
#print(subset(model_diags_df, .hatvalues > 0.99)[, glbFeatsId])
#dffits_df <- merge(dffits_df, outliers_df, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#dffits_df <- merge(dffits_df, glbObsFit, by="row.names", all.x=TRUE); row.names(dffits_df) <- dffits_df$Row.names; dffits_df <- subset(dffits_df, select=-Row.names)
#subset(dffits_df, !is.na(.Bonf.p))
#mdlId <- "CSM.X.glm"; vars <- myextract_actual_feats(row.names(orderBy(reformulate(c("-", paste0(mdlId, ".imp"))), myget_feats_imp(glb_models_lst[[mdlId]]))));
#model_diags_df <- glb_get_predictions(model_diags_df, mdlId, glb_rsp_var)
#obs_ix <- row.names(model_diags_df) %in% names(outliers$rstudent)[1]
#obs_ix <- which(is.na(model_diags_df$.rstudent))
#obs_ix <- which(is.na(model_diags_df$.dffits))
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, paste0(glb_rsp_var, mdlId), vars[1:min(20, length(vars))])], obs_ix=obs_ix, id_var=glbFeatsId, category_var=glbFeatsCategory)
#model_diags_df[row.names(model_diags_df) %in% names(outliers$rstudent)[c(1:2)], ]
#ctgry_diags_df <- model_diags_df[model_diags_df[, glbFeatsCategory] %in% c("Unknown#0"), ]
#myplot_parcoord(obs_df=ctgry_diags_df[, c(glbFeatsId, glbFeatsCategory, ".rstudent", ".dffits", ".hatvalues", glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:20])], obs_ix=row.names(ctgry_diags_df) %in% names(outliers$rstudent)[1], id_var=glbFeatsId, category_var=glbFeatsCategory)
#table(glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), "startprice.log10.cut.fctr"])
#glbObsFit[model_diags_df[, glbFeatsCategory] %in% c("iPad1#1"), c(glbFeatsId, "startprice")]
# No outliers & .dffits == NaN
#myplot_parcoord(obs_df=model_diags_df[, c(glbFeatsId, glbFeatsCategory, glb_rsp_var, "startprice.log10.predict.RFE.X.glmnet", indepVar[1:10])], obs_ix=seq(1:nrow(model_diags_df))[is.na(model_diags_df$.dffits)], id_var=glbFeatsId, category_var=glbFeatsCategory)
# Modify mdlId to (build & extract) "<FamilyId>#<Fit|Trn>#<caretMethod>#<preProc1.preProc2>#<samplingMethod>"
glb_models_lst <- list(); glb_models_df <- data.frame()
# Add xgboost algorithm
# Regression
if (glb_is_regression) {
glbMdlMethods <- c(NULL
# deterministic
#, "lm", # same as glm
, "glm", "bayesglm", "glmnet"
, "rpart"
# non-deterministic
, "gbm", "rf"
# Unknown
, "nnet" , "avNNet" # runs 25 models per cv sample for tunelength=5
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
, "bagEarth" # Takes a long time
,"xgbLinear","xgbTree"
)
} else
# Classification - Add ada (auto feature selection)
if (glb_is_binomial)
glbMdlMethods <- c(NULL
# deterministic
, "bagEarth" # Takes a long time
, "glm", "bayesglm", "glmnet"
, "nnet"
, "rpart"
# non-deterministic
, "gbm"
, "avNNet" # runs 25 models per cv sample for tunelength=5
, "rf"
# Unknown
, "lda", "lda2"
# svm models crash when predict is called -> internal to kernlab it should call predict without .outcome
, "svmLinear", "svmLinear2"
, "svmPoly" # runs 75 models per cv sample for tunelength=5
, "svmRadial"
, "earth"
,"xgbLinear","xgbTree"
) else
glbMdlMethods <- c(NULL
# deterministic
,"glmnet"
# non-deterministic
,"rf"
# Unknown
,"gbm","rpart","xgbLinear","xgbTree"
)
glbMdlFamilies <- list(); glb_mdl_feats_lst <- list()
# family: Choose from c("RFE.X", "CSM.X", "All.X", "Best.Interact")
# methods: Choose from c(NULL, <method>, glbMdlMethods)
#glbMdlFamilies[["RFE.X"]] <- c("glmnet", "glm") # non-NULL vector is mandatory
if (glb_is_classification && !glb_is_binomial)
# glm does not work for multinomial
glbMdlFamilies[["All.X"]] <- c("glmnet") else
glbMdlFamilies[["All.X"]] <- c("glmnet", "glm")
#glbMdlFamilies[["Best.Interact"]] <- "glmnet" # non-NULL vector is mandatory
# Check if interaction features make RFE better
# glbMdlFamilies[["CSM.X"]] <- setdiff(glbMdlMethods, c("lda", "lda2")) # crashing due to category:.clusterid ??? #c("glmnet", "glm") # non-NULL list is mandatory
# glb_mdl_feats_lst[["CSM.X"]] <- c(NULL
# , <comma-separated-features-vector>
# )
# dAFeats.CSM.X %<d-% c(NULL
# # Interaction feats up to varImp(RFE.X.glmnet) >= 50
# , <comma-separated-features-vector>
# , setdiff(myextract_actual_feats(predictors(rfe_fit_results)), c(NULL
# , <comma-separated-features-vector>
# ))
# )
# glb_mdl_feats_lst[["CSM.X"]] <- "%<d-% dAFeats.CSM.X"
glbMdlFamilies[["Final"]] <- c(NULL) # NULL vector acceptable # c("glmnet", "glm")
glbMdlAllowParallel <- list()
#glbMdlAllowParallel[["Final##rcv#glmnet"]] <- FALSE
glbMdlAllowParallel[["All.X##rcv#glm"]] <- FALSE
# Check if tuning parameters make fit better; make it mdlFamily customizable ?
glbMdlTuneParams <- data.frame()
# When glmnet crashes at model$grid with error: ???
glmnetTuneParams <- rbind(data.frame()
,data.frame(parameter = "alpha", vals = "0.100 0.325 0.550 0.775 1.000")
,data.frame(parameter = "lambda", vals = "9.342e-02")
)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams,
# cbind(data.frame(mdlId = "<mdlId>"),
# glmnetTuneParams))
#avNNet
# size=[1] 3 5 7 9; decay=[0] 1e-04 0.001 0.01 0.1; bag=[FALSE]; RMSE=1.3300906
#bagEarth
# degree=1 [2] 3; nprune=64 128 256 512 [1024]; RMSE=0.6486663 (up)
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "bagEarth", parameter = "nprune", vals = "256")
# ,data.frame(method = "bagEarth", parameter = "degree", vals = "2")
# ))
#earth
# degree=[1]; nprune=2 [9] 17 25 33; RMSE=0.1334478
#gbm
# shrinkage=0.05 [0.10] 0.15 0.20 0.25; n.trees=100 150 200 [250] 300; interaction.depth=[1] 2 3 4 5; n.minobsinnode=[10]; RMSE=0.2008313
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "gbm", parameter = "shrinkage", min = 0.05, max = 0.25, by = 0.05)
# ,data.frame(method = "gbm", parameter = "n.trees", min = 100, max = 300, by = 50)
# ,data.frame(method = "gbm", parameter = "interaction.depth", min = 1, max = 5, by = 1)
# ,data.frame(method = "gbm", parameter = "n.minobsinnode", min = 10, max = 10, by = 10)
# #seq(from=0.05, to=0.25, by=0.05)
# ))
#glmnet
# alpha=0.100 [0.325] 0.550 0.775 1.000; lambda=0.0005232693 0.0024288010 0.0112734954 [0.0523269304] 0.2428800957; RMSE=0.6164891
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha", vals = "0.550 0.775 0.8875 0.94375 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda", vals = "9.858855e-05 0.0001971771 0.0009152152 0.0042480525 0.0197177130")
# ))
#nnet
# size=3 5 [7] 9 11; decay=0.0001 0.001 0.01 [0.1] 0.2; RMSE=0.9287422
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "nnet", parameter = "size", vals = "3 5 7 9 11")
# ,data.frame(method = "nnet", parameter = "decay", vals = "0.0001 0.0010 0.0100 0.1000 0.2000")
# ))
#rf # Don't bother; results are not deterministic
# mtry=2 35 68 [101] 134; RMSE=0.1339974
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rf", parameter = "mtry", vals = "2 5 9 13 17")
# ))
#rpart
# cp=0.020 [0.025] 0.030 0.035 0.040; RMSE=0.1770237
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "rpart", parameter = "cp", vals = "0.004347826 0.008695652 0.017391304 0.021739130 0.034782609")
# ))
#svmLinear
# C=0.01 0.05 [0.10] 0.50 1.00 2.00 3.00 4.00; RMSE=0.1271318; 0.1296718
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear", parameter = "C", vals = "0.01 0.05 0.1 0.5 1")
# ))
#svmLinear2
# cost=0.0625 0.1250 [0.25] 0.50 1.00; RMSE=0.1276354
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method = "svmLinear2", parameter = "cost", vals = "0.0625 0.125 0.25 0.5 1")
# ))
#svmPoly
# degree=[1] 2 3 4 5; scale=0.01 0.05 [0.1] 0.5 1; C=0.50 1.00 [2.00] 3.00 4.00; RMSE=0.1276130
# glbMdlTuneParams <- myrbind_df(glbMdlTuneParams, rbind(data.frame()
# ,data.frame(method="svmPoly", parameter="degree", min=1, max=5, by=1) #seq(1, 5, 1)
# ,data.frame(method="svmPoly", parameter="scale", vals="0.01, 0.05, 0.1, 0.5, 1")
# ,data.frame(method="svmPoly", parameter="C", vals="0.50, 1.00, 2.00, 3.00, 4.00")
# ))
#svmRadial
# sigma=[0.08674323]; C=0.25 0.50 1.00 [2.00] 4.00; RMSE=0.1614957
#glb2Sav(); all.equal(sav_models_df, glb_models_df)
glb_preproc_methods <- NULL
# c("YeoJohnson", "center.scale", "range", "pca", "ica", "spatialSign")
# Baseline prediction model feature(s)
glb_Baseline_mdl_var <- NULL # or c("<feat>")
glbMdlMetric_terms <- NULL # or matrix(c(
# 0,1,2,3,4,
# 2,0,1,2,3,
# 4,2,0,1,2,
# 6,4,2,0,1,
# 8,6,4,2,0
# ), byrow=TRUE, nrow=5)
glbMdlMetricSummary <- NULL # or "<metric_name>"
glbMdlMetricMaximize <- NULL # or FALSE (TRUE is not the default for both classification & regression)
glbMdlMetricSummaryFn <- NULL # or function(data, lev=NULL, model=NULL) {
# confusion_mtrx <- t(as.matrix(confusionMatrix(data$pred, data$obs)))
# #print(confusion_mtrx)
# #print(confusion_mtrx * glbMdlMetric_terms)
# metric <- sum(confusion_mtrx * glbMdlMetric_terms) / nrow(data)
# names(metric) <- glbMdlMetricSummary
# return(metric)
# }
glbMdlCheckRcv <- FALSE # Turn it on when needed; otherwise takes long time
glb_rcv_n_folds <- 3 # or NULL
glb_rcv_n_repeats <- 3 # or NULL
glb_clf_proba_threshold <- NULL # 0.5
# Model selection criteria
if (glb_is_regression)
glbMdlMetricsEval <- c("min.RMSE.OOB", "max.R.sq.OOB", "max.Adj.R.sq.fit", "min.RMSE.fit")
#glbMdlMetricsEval <- c("min.RMSE.fit", "max.R.sq.fit", "max.Adj.R.sq.fit")
if (glb_is_classification) {
if (glb_is_binomial)
glbMdlMetricsEval <-
c("max.Accuracy.OOB", "max.AUCROCR.OOB", "max.AUCpROC.OOB", "min.aic.fit", "max.Accuracy.fit") else
glbMdlMetricsEval <- c("max.Accuracy.OOB", "max.Kappa.OOB")
}
# select from NULL [no ensemble models], "auto" [all models better than MFO or Baseline], c(mdl_ids in glb_models_lst) [Typically top-rated models in auto]
glb_mdl_ensemble <- NULL
# "%<d-% setdiff(mygetEnsembleAutoMdlIds(), 'CSM.X.rf')"
# c(<comma-separated-mdlIds>
# )
# Only for classifications; for regressions remove "(.*)\\.prob" form the regex
# tmp_fitobs_df <- glbObsFit[, grep(paste0("^", gsub(".", "\\.", mygetPredictIds$value, fixed = TRUE), "CSM\\.X\\.(.*)\\.prob"), names(glbObsFit), value = TRUE)]; cor_mtrx <- cor(tmp_fitobs_df); cor_vctr <- sort(cor_mtrx[row.names(orderBy(~-Overall, varImp(glb_models_lst[["Ensemble.repeatedcv.glmnet"]])$imp))[1], ]); summary(cor_vctr); cor_vctr
#ntv.glm <- glm(reformulate(indepVar, glb_rsp_var), family = "binomial", data = glbObsFit)
#step.glm <- step(ntv.glm)
glbMdlSelId <- "All.X##rcv#glmnet" #select from c(NULL, "All.X##rcv#glmnet", "RFE.X##rcv#glmnet", <mdlId>)
glbMdlFinId <- NULL #select from c(NULL, glbMdlSelId)
glb_dsp_cols <- c(".pos", glbFeatsId, glbFeatsCategory, glb_rsp_var
# List critical cols excl. above
)
# Output specs
# lclgetfltout_df <- function(obsOutFinDf) {
# require(tidyr)
# obsOutFinDf <- obsOutFinDf %>%
# tidyr::separate("ImageId.x.y", c(".src", ".pos", "x", "y"),
# sep = "#", remove = TRUE, extra = "merge")
# # mnm prefix stands for max_n_mean
# mnmout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# #dplyr::top_n(1, Probability1) %>% # Score = 3.9426
# #dplyr::top_n(2, Probability1) %>% # Score = ???; weighted = 3.94254;
# #dplyr::top_n(3, Probability1) %>% # Score = 3.9418; weighted = 3.94169;
# dplyr::top_n(4, Probability1) %>% # Score = ???; weighted = 3.94149;
# #dplyr::top_n(5, Probability1) %>% # Score = 3.9421; weighted = 3.94178
#
# # dplyr::summarize(xMeanN = mean(as.numeric(x)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), Probability1), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1, 0.2357323, 0.2336925)), yMeanN = mean(as.numeric(y)))
# # dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)), yMeanN = mean(as.numeric(y)))
# dplyr::summarize(xMeanN = weighted.mean(as.numeric(x), c(Probability1)),
# yMeanN = weighted.mean(as.numeric(y), c(Probability1)))
#
# maxout_df <- obsOutFinDf %>%
# dplyr::group_by(.pos) %>%
# dplyr::summarize(maxProb1 = max(Probability1))
# fltout_df <- merge(maxout_df, obsOutFinDf,
# by.x = c(".pos", "maxProb1"), by.y = c(".pos", "Probability1"),
# all.x = TRUE)
# fmnout_df <- merge(fltout_df, mnmout_df,
# by.x = c(".pos"), by.y = c(".pos"),
# all.x = TRUE)
# return(fmnout_df)
# }
glbObsOut <- list(NULL
# glbFeatsId will be the first output column, by default
,vars = list()
# ,mapFn = function(obsOutFinDf) {
# }
)
#obsOutFinDf <- savobsOutFinDf
# glbObsOut$mapFn <- function(obsOutFinDf) {
# txfout_df <- dplyr::select(obsOutFinDf, -.pos.y) %>%
# dplyr::mutate(
# lunch = levels(glbObsTrn[, "lunch" ])[
# round(mean(as.numeric(glbObsTrn[, "lunch" ])), 0)],
# dinner = levels(glbObsTrn[, "dinner" ])[
# round(mean(as.numeric(glbObsTrn[, "dinner" ])), 0)],
# reserve = levels(glbObsTrn[, "reserve" ])[
# round(mean(as.numeric(glbObsTrn[, "reserve" ])), 0)],
# outdoor = levels(glbObsTrn[, "outdoor" ])[
# round(mean(as.numeric(glbObsTrn[, "outdoor" ])), 0)],
# expensive = levels(glbObsTrn[, "expensive"])[
# round(mean(as.numeric(glbObsTrn[, "expensive"])), 0)],
# liquor = levels(glbObsTrn[, "liquor" ])[
# round(mean(as.numeric(glbObsTrn[, "liquor" ])), 0)],
# table = levels(glbObsTrn[, "table" ])[
# round(mean(as.numeric(glbObsTrn[, "table" ])), 0)],
# classy = levels(glbObsTrn[, "classy" ])[
# round(mean(as.numeric(glbObsTrn[, "classy" ])), 0)],
# kids = levels(glbObsTrn[, "kids" ])[
# round(mean(as.numeric(glbObsTrn[, "kids" ])), 0)]
# )
#
# print("ObsNew output class tables:")
# print(sapply(c("lunch","dinner","reserve","outdoor",
# "expensive","liquor","table",
# "classy","kids"),
# function(feat) table(txfout_df[, feat], useNA = "ifany")))
#
# txfout_df <- txfout_df %>%
# dplyr::mutate(labels = "") %>%
# dplyr::mutate(labels =
# ifelse(lunch != "-1", paste(labels, lunch ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(dinner != "-1", paste(labels, dinner ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(reserve != "-1", paste(labels, reserve ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(outdoor != "-1", paste(labels, outdoor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(expensive != "-1", paste(labels, expensive), labels)) %>%
# dplyr::mutate(labels =
# ifelse(liquor != "-1", paste(labels, liquor ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(table != "-1", paste(labels, table ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(classy != "-1", paste(labels, classy ), labels)) %>%
# dplyr::mutate(labels =
# ifelse(kids != "-1", paste(labels, kids ), labels)) %>%
# dplyr::select(business_id, labels)
# return(txfout_df)
# }
#if (!is.null(glbObsOut$mapFn)) obsOutFinDf <- glbObsOut$mapFn(obsOutFinDf); print(head(obsOutFinDf))
glb_out_obs <- NULL # select from c(NULL : default to "new", "all", "new", "trn")
if (glb_is_classification && glb_is_binomial) {
# glbObsOut$vars[["Probability1"]] <-
# "%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$prob]"
# glbObsOut$vars[[glb_rsp_var_raw]] <-
# "%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
# mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
glbObsOut$vars[["Predictions"]] <-
"%<d-% glb_map_rsp_var_to_raw(glbObsNew[,
mygetPredictIds(glb_rsp_var, glbMdlFinId)$value])"
} else {
# glbObsOut$vars[[glbFeatsId]] <-
# "%<d-% as.integer(gsub('Test#', '', glbObsNew[, glbFeatsId]))"
glbObsOut$vars[[glb_rsp_var]] <-
"%<d-% glbObsNew[, mygetPredictIds(glb_rsp_var, glbMdlFinId)$value]"
# for (outVar in setdiff(glbFeatsExcludeLcl, glb_rsp_var_raw))
# glbObsOut$vars[[outVar]] <-
# paste0("%<d-% mean(glbObsAll[, \"", outVar, "\"], na.rm = TRUE)")
}
# glbObsOut$vars[[glb_rsp_var_raw]] <- glb_rsp_var_raw
# glbObsOut$vars[[paste0(head(unlist(strsplit(mygetPredictIds$value, "")), -1), collapse = "")]] <-
glbOutStackFnames <- NULL #: default
# c("ebayipads_txt_assoc1_out_bid1_stack.csv") # manual stack
# c("ebayipads_finmdl_bid1_out_nnet_1.csv") # universal stack
glbOut <- list(pfx = "Votes_Q_02_cluster_")
# lclImageSampleSeed <- 129
glbOutDataVizFname <- NULL # choose from c(NULL, "<projectId>_obsall.csv")
glbChunks <- list(labels = c("set_global_options_wd","set_global_options"
,"import.data","inspect.data","scrub.data","transform.data"
,"extract.features"
,"extract.features.datetime","extract.features.image","extract.features.price"
,"extract.features.text","extract.features.string"
,"extract.features.end"
,"manage.missing.data","cluster.data","partition.data.training","select.features"
,"fit.models_0","fit.models_1","fit.models_2","fit.models_3"
,"fit.data.training_0","fit.data.training_1"
,"predict.data.new"
,"display.session.info"))
# To ensure that all chunks in this script are in glbChunks
if (!is.null(chkChunksLabels <- knitr::all_labels()) && # knitr::all_labels() doesn't work in console runs
!identical(chkChunksLabels, glbChunks$labels)) {
print(sprintf("setdiff(chkChunksLabels, glbChunks$labels): %s",
setdiff(chkChunksLabels, glbChunks$labels)))
print(sprintf("setdiff(glbChunks$labels, chkChunksLabels): %s",
setdiff(glbChunks$labels, chkChunksLabels)))
}
glbChunks[["first"]] <- "cluster.data" #default: script will load envir from previous chunk
glbChunks[["last"]] <- NULL #NULL #default: script will save envir at end of this chunk
glbChunks[["inpFilePathName"]] <- "data/Votes_Q_01_cnk_manage.missing.data.RData"
#mysavChunk(glbOut$pfx, glbChunks[["last"]]) # called from myevlChunk
# Inspect max OOB FP
#chkObsOOB <- subset(glbObsOOB, !label.fctr.All.X..rcv.glmnet.is.acc)
#chkObsOOBFP <- subset(chkObsOOB, label.fctr.All.X..rcv.glmnet == "left_eye_center") %>% dplyr::mutate(Probability1 = label.fctr.All.X..rcv.glmnet.prob) %>% select(-.src, -.pos, -x, -y) %>% lclgetfltout_df() %>% mutate(obj.distance = (((as.numeric(x) - left_eye_center_x.int) ^ 2) + ((as.numeric(y) - left_eye_center_y.int) ^ 2)) ^ 0.5) %>% dplyr::top_n(5, obj.distance) %>% dplyr::top_n(5, -patch.cor)
#
#newImgObs <- glbObsNew[(glbObsNew$ImageId == "Test#0001"), ]; print(newImgObs[which.max(newImgObs$label.fctr.Final..rcv.glmnet.prob), ])
#OOBImgObs <- glbObsOOB[(glbObsOOB$ImageId == "Train#0003"), ]; print(OOBImgObs[which.max(OOBImgObs$label.fctr.All.X..rcv.glmnet.prob), ])
#load("Votes_Q_02_cnk_extract.features.end.RData", verbose = TRUE)
#mygetImage(which(glbObsAll[, glbFeatsId] == "Train#0003"), names(glbFeatsImage)[1], plot = TRUE, featHighlight = c("left_eye_center_x", "left_eye_center_y"), ovrlHighlight = c(66, 35))
# Depict process
glb_analytics_pn <- petrinet(name = "glb_analytics_pn",
trans_df = data.frame(id = 1:6,
name = c("data.training.all","data.new",
"model.selected","model.final",
"data.training.all.prediction","data.new.prediction"),
x=c( -5,-5,-15,-25,-25,-35),
y=c( -5, 5, 0, 0, -5, 5)
),
places_df=data.frame(id=1:4,
name=c("bgn","fit.data.training.all","predict.data.new","end"),
x=c( -0, -20, -30, -40),
y=c( 0, 0, 0, 0),
M0=c( 3, 0, 0, 0)
),
arcs_df = data.frame(
begin = c("bgn","bgn","bgn",
"data.training.all","model.selected","fit.data.training.all",
"fit.data.training.all","model.final",
"data.new","predict.data.new",
"data.training.all.prediction","data.new.prediction"),
end = c("data.training.all","data.new","model.selected",
"fit.data.training.all","fit.data.training.all","model.final",
"data.training.all.prediction","predict.data.new",
"predict.data.new","data.new.prediction",
"end","end")
))
#print(ggplot.petrinet(glb_analytics_pn))
print(ggplot.petrinet(glb_analytics_pn) + coord_flip())
## Loading required package: grid
glb_analytics_avl_objs <- NULL
glb_chunks_df <- myadd_chunk(NULL,
ifelse(is.null(glbChunks$first), "import.data", glbChunks$first))
## label step_major step_minor label_minor bgn end elapsed
## 1 cluster.data 1 0 0 6.109 NA NA
1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data1.0: cluster data{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx)} #{r cluster.data, cache=FALSE, echo=FALSE, eval=myevlChunk(glbChunks, glbOut$pfx, keep = c(“glbFeatsCategory”,“glb_dsp_cols”))}## Loading required package: proxy
##
## Attaching package: 'proxy'
## The following objects are masked from 'package:stats':
##
## as.dist, dist
## The following object is masked from 'package:base':
##
## as.matrix
## Loading required package: dynamicTreeCut
## Loading required package: entropy
## Loading required package: tidyr
## Loading required package: ggdendro
## [1] "Clustering features: "
## abs.cor.y
## Q98197.fctr 0.05493425
## Q113181.fctr 0.08087531
## Q115611.fctr 0.09044682
## Gender.fctr 0.10274009
## Q109244.fctr 0.12038125
## [1] " .rnorm cor: -0.0078"
## [1] " Clustering entropy measure: Party.fctr"
## [1] "glbObsAll Entropy: 0.6913"
## Loading required package: lazyeval
## Hhold.fctr .clusterid Hhold.fctr.clusterid R D .entropy .knt
## 1 N 1 N_1 220 230 0.6929002 450
## 2 MKn 1 MKn_1 308 344 0.6916221 652
## 3 MKy 1 MKy_1 842 752 0.6915524 1594
## 4 PKn 1 PKn_1 49 131 0.5854566 180
## 5 PKy 1 PKy_1 26 35 0.6822232 61
## 6 SKn 1 SKn_1 1091 1340 0.6878923 2431
## 7 SKy 1 SKy_1 81 119 0.6749870 200
## [1] "glbObsAll$Hhold.fctr Entropy: 0.6859 (99.2186 pct)"
## [1] "Category: N"
## [1] "max distance(0.9799) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 2175 2711 D N (50,65] N >150K
## 3742 4664 D N NA N N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 2175 Bcr NA NA NA NA
## 3742 N NA Yes NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 2175 NA NA NA NA NA
## 3742 Pc Yes Yes NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 2175 NA NA NA NA NA
## 3742 NA No Yes Yes Science
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 2175 NA NA NA NA NA
## 3742 Yes Study first No NA No
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 2175 NA NA Yes NA NA
## 3742 Giving Yes NA Yes No
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 2175 NA NA NA NA NA
## 3742 Pr No Standard hours NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 2175 NA NA NA NA NA
## 3742 NA NA Yes NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 2175 NA NA NA NA NA
## 3742 NA NA End NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 2175 NA NA NA NA NA
## 3742 Me Yes No NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 2175 NA NA Yes NA NA
## 3742 No NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 2175 Technology NA NA NA NA
## 3742 NA NA No Yes No
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 2175 Yes Supportive NA NA NA
## 3742 Yes Demanding No NA Yes
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 2175 NA NA NA NA NA
## 3742 NA Cautious NA No NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 2175 NA NA NA NA NA
## 3742 NA In-person No Yes NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 2175 NA NA NA NA NA
## 3742 Yes Yy Yes Yes No
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 2175 NA NA NA NA NA
## 3742 NA No Yes Yes No
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 2175 Yes No NA NA NA
## 3742 No Yes No Yes NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 2175 NA NA NA NA NA
## 3742 NA NA Yes Yes No
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 2175 Yes NA NA NA NA
## 3742 Yes Yes Nope NA No
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 2175 NA NA NA NA NA
## 3742 Yes NA NA NA Yes
## Q98078.fctr Q96024.fctr
## 2175 NA NA
## 3742 No NA
## [1] "min distance(0.9403) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 1286 1591 R N (15,20] M N
## 2641 3283 R N (15,20] M N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 1286 K12 NA NA NA NA
## 2641 N NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 1286 NA NA NA NA No
## 2641 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 1286 No Mysterious NA NA NA
## 2641 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 1286 NA Yes NA NA NA
## 2641 NA Yes Yes Yes Yes
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 1286 NA NA NA NA NA
## 2641 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 1286 NA NA
## 2641 NA NA
## [1] "Category: MKn"
## [1] "max distance(0.9773) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 2328 2894 D MKn (65,90] M 75-100K
## 4767 5946 R MKn (65,90] N N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 2328 N NA No No No
## 4767 Msr No NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 2328 Pc Yes Yes No No
## 4767 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 2328 Yes Yes No Yes Science
## 4767 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 2328 No Study first Yes Yes Yes
## 4767 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 2328 Receiving Yes No NA NA
## 4767 NA NA No No No
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 2328 NA NA NA NA NA
## 4767 Pr Yes NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 2328 NA NA NA NA NA
## 4767 NA Yes NA NA Yes
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 2328 NA NA NA NA NA
## 4767 Yes Supportive No Mac Yes
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA Yes! No Space
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 2328 NA NA NA NA NA
## 4767 No In-person No NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 2328 NA NA NA NA NA
## 4767 Yes Yy NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 2328 NA NA NA NA NA
## 4767 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 2328 NA NA NA NA NA
## 4767 Optimist Dad NA Yes Yes
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 2328 NA Yes Check! No No
## 4767 Yes NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 2328 Yes Yes Yes NA NA
## 4767 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 2328 NA NA
## 4767 NA NA
## [1] "min distance(0.9482) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 3363 4185 D MKn (30,35] F 100-150K
## 4551 5680 D MKn (25,30] F 25-50K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 3363 Bcr NA NA NA NA
## 4551 Bcr NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA Standard hours NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 3363 NA NA NA NA NA
## 4551 NA Yes NA NA No
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 3363 Yes NA NA NA NA
## 4551 Yes Cautious Umm... NA Space
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA Yes NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 3363 NA NA NA NA NA
## 4551 NA Yes Yes NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 3363 NA NA NA NA NA
## 4551 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 3363 NA NA
## 4551 NA NA
## [1] "Category: MKy"
## [1] "max distance(0.9786) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 3492 4349 R MKy (65,90] F 100-150K
## 4106 5121 R MKy NA N N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 3492 N NA Yes NA NA
## 4106 Msr NA NA Yes NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 3492 Pt Yes NA NA NA
## 4106 Pt NA NA No NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 3492 NA NA NA NA NA
## 4106 NA NA Yes Yes Science
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 3492 NA NA NA Yes NA
## 4106 Yes Study first No Yes Yes
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 3492 NA NA NA NA NA
## 4106 Giving NA Yes NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 3492 NA NA NA Hot headed Yes
## 4106 NA No Standard hours NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 3492 NA NA Yes No NA
## 4106 NA Yes Yes Yes No
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 3492 A.M. NA NA Yes NA
## 4106 A.M. Yes NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 3492 Me NA NA Yes NA
## 4106 NA NA No NA Yes
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 3492 Yes NA NA NA Talk
## 4106 NA NA NA NA Tunes
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 3492 NA NA Yes Yes NA
## 4106 People NA Yes NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 3492 NA NA NA Mac NA
## 4106 Yes NA NA NA No
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 3492 NA NA Umm... NA Space
## 4106 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 3492 NA Online NA NA Yes
## 4106 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 3492 NA NA NA NA NA
## 4106 No NA Yes No NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 3492 Yes NA NA NA NA
## 4106 NA No Yes Yes NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 3492 NA NA NA NA Own
## 4106 NA NA Yes NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 3492 Pessimist NA NA NA NA
## 4106 Optimist NA No Yes Yes
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 3492 NA NA NA NA NA
## 4106 NA No Check! No No
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 3492 NA NA NA NA NA
## 4106 Yes NA Yes NA Yes
## Q98078.fctr Q96024.fctr
## 3492 NA NA
## 4106 NA Yes
## [1] "min distance(0.9489) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 4998 6245 R MKy (15,20] M 50-75K
## 6032 2409 <NA> MKy (35,40] M <25K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 4998 N NA NA NA NA
## 6032 N NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 4998 NA NA NA NA Science
## 6032 NA NA NA NA Art
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 4998 Yes Try first NA Yes Yes
## 6032 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 4998 Giving NA NA NA NA
## 6032 NA NA No No No
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 4998 Pr NA NA Cool headed NA
## 6032 Id Yes NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 4998 Right No NA NA NA
## 6032 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 4998 NA Supportive NA NA NA
## 6032 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 4998 Yes NA NA Yes NA
## 6032 No NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 4998 Yes NA NA NA NA
## 6032 NA NA NA No No
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA Yes
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 4998 NA NA NA NA NA
## 6032 Yes Yes Nope No Yes
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 4998 NA NA NA NA NA
## 6032 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 4998 NA NA
## 6032 NA NA
## [1] "Category: PKn"
## [1] "max distance(0.9742) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 404 502 D PKn (40,50] F 75-100K
## 6145 2930 <NA> PKn (50,65] F 25-50K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 404 PhD NA Yes NA NA
## 6145 N NA NA Yes No
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 404 NA NA NA NA No
## 6145 Pc No Yes No No
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 404 Yes Yes Yes NA NA
## 6145 Yes No Yes Yes Art
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 404 NA NA NA NA NA
## 6145 Yes Try first Yes Yes Yes
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 404 NA NA NA NA NA
## 6145 Giving No NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 404 NA Yes Yes Yes NA
## 6145 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 404 P.M. NA NA NA No
## 6145 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 404 NA NA NA No Yes
## 6145 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 404 NA NA NA NA NA
## 6145 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 404 NA Yes Yes Yes Yes
## 6145 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 404 No No No No Rent
## 6145 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 404 Optimist Mom No Yes NA
## 6145 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 404 Yes NA Check! No Yes
## 6145 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 404 Yes NA NA Yes Yes
## 6145 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 404 Yes No
## 6145 NA NA
## [1] "min distance(0.9462) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 848 1046 D PKn (50,65] M 100-150K
## 3463 4312 D PKn (20,25] F 25-50K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 848 N NA NA NA NA
## 3463 HSD NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 848 NA NA NA NA Yes
## 3463 NA NA Yes Mac Yes
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 848 Yes Risk-friendly Yes! No Space
## 3463 Yes Risk-friendly Yes! No Space
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 848 No In-person Yes NA NA
## 3463 Yes NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 848 NA NA NA NA NA
## 3463 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 848 NA No
## 3463 NA NA
## [1] "Category: PKy"
## [1] "max distance(0.9739) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 1561 1933 R PKy (50,65] F >150K
## 4384 5471 R PKy (30,35] F 100-150K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 1561 Ast NA NA NA NA
## 4384 HSD Yes Yes Yes No
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 1561 NA NA NA NA NA
## 4384 Pc No Yes No Yes
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 1561 NA NA NA NA NA
## 4384 Yes No Yes Yes Art
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 1561 NA NA NA NA NA
## 4384 No Study first Yes Yes Yes
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 1561 NA NA NA Yes Yes
## 4384 Giving Yes No No Yes
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 1561 Pr No NA Cool headed No
## 4384 Id No Standard hours Hot headed No
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 1561 Happy No Yes No No
## 4384 Right No NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 1561 A.M. Yes Start Yes NA
## 4384 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 1561 Cs Yes No No Yes
## 4384 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 1561 Yes Mysterious No No NA
## 4384 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 1561 NA NA NA NA NA
## 4384 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 1561 NA NA NA NA NA
## 4384 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 1561 NA NA NA NA NA
## 4384 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 1561 NA NA NA NA NA
## 4384 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 1561 NA NA Yes No No
## 4384 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 1561 No Yes No NA NA
## 4384 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 1561 NA NA NA NA NA
## 4384 NA Yes No Yes Rent
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 1561 NA NA NA Yes Yes
## 4384 Optimist Dad Yes Yes NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 1561 No Yes Check! No No
## 4384 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 1561 Yes Yes No No Yes
## 4384 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 1561 Yes Yes
## 4384 NA No
## [1] "min distance(0.9528) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 3332 4150 D PKy (25,30] M 50-75K
## 6815 6244 <NA> PKy (40,50] M N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 3332 HSD NA NA NA NA
## 6815 Ast NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 3332 Pc No Yes NA No
## 6815 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 3332 No No No NA Science
## 6815 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 3332 No NA NA No No
## 6815 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 3332 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 3332 NA NA Odd hours Cool headed NA
## 6815 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 3332 Right NA NA NA NA
## 6815 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 3332 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 3332 NA NA NA NA No
## 6815 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 3332 No NA NA No NA
## 6815 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 3332 NA No Yes Yes NA
## 6815 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 3332 NA NA NA NA No
## 6815 NA NA NA PC Yes
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 3332 No Cautious NA No NA
## 6815 Yes Risk-friendly Umm... No Space
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 3332 NA In-person Yes NA NA
## 6815 No NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 3332 Yes Gr NA NA NA
## 6815 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 3332 NA NA NA No NA
## 6815 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 3332 Yes NA No NA NA
## 6815 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 3332 Pessimist NA NA Yes NA
## 6815 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 3332 No NA NA NA NA
## 6815 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 3332 NA NA NA NA NA
## 6815 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 3332 NA NA
## 6815 NA NA
## [1] "Category: SKn"
## [1] "max distance(0.9784) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 5083 6347 R SKn NA N >150K
## 6482 4629 <NA> SKn (30,35] N 50-75K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 5083 PhD NA NA NA NA
## 6482 N NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 5083 Pt NA NA NA NA
## 6482 NA NA NA NA No
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 5083 NA NA NA NA NA
## 6482 Yes Yes Yes Yes Art
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 5083 Yes NA NA NA NA
## 6482 No Study first No No No
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 5083 NA NA NA NA NA
## 6482 Receiving Yes NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 5083 NA NA NA NA No
## 6482 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 5083 NA NA NA NA Yes
## 6482 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA NA Yes Yes
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 5083 NA NA NA NA NA
## 6482 NA NA Yes No No
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 5083 NA NA NA NA NA
## 6482 Yes No Yes No No
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 5083 NA NA NA NA NA
## 6482 No No Yes Yes Rent
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 5083 NA NA NA NA NA
## 6482 Optimist Mom Yes NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 5083 NA NA NA NA NA
## 6482 NA Yes Check! No No
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 5083 NA NA NA NA NA
## 6482 Yes Yes No No Only-child
## Q98078.fctr Q96024.fctr
## 5083 NA NA
## 6482 Yes NA
## [1] "min distance(0.9355) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 2712 3375 D SKn (25,30] M <25K
## 4692 5856 R SKn (25,30] M N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 2712 HSD NA NA NA NA
## 4692 K12 NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 2712 NA NA NA NA No
## 4692 NA NA No PC Yes
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 2712 Yes Cautious NA NA NA
## 4692 Yes Risk-friendly Umm... NA Space
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 2712 No NA NA NA NA
## 4692 Yes NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 2712 NA NA NA NA NA
## 4692 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 2712 NA NA
## 4692 NA No
## [1] "Category: SKy"
## [1] "max distance(0.9771) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 3012 3748 R SKy (50,65] F 75-100K
## 5348 6679 D SKy (40,50] N 75-100K
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 3012 N NA NA NA NA
## 5348 PhD Yes No Yes No
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 3012 NA NA NA NA NA
## 5348 Pc No Yes No No
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 3012 NA NA NA Yes Art
## 5348 Yes No Yes Yes Art
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 3012 No Study first Yes No Yes
## 5348 No NA NA Yes NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 3012 Giving No Yes No Yes
## 5348 NA NA Yes NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 3012 Pr No NA NA NA
## 5348 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 3012 NA NA NA NA NA
## 5348 NA NA NA NA NA
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 3012 NA Yes Start Yes Yes
## 5348 NA NA NA NA NA
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 3012 Me No No No No
## 5348 NA NA NA NA NA
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 3012 No TMI Yes No Tunes
## 5348 NA NA NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 3012 People NA NA NA No
## 5348 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 3012 Yes Demanding No PC NA
## 5348 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 3012 NA NA NA NA NA
## 5348 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 3012 NA NA NA NA NA
## 5348 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 3012 NA NA NA NA NA
## 5348 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 3012 NA Yes Yes Yes No
## 5348 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 3012 NA NA NA NA Own
## 5348 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 3012 Optimist Dad No Yes No
## 5348 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 3012 No Yes Check! Yes No
## 5348 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 3012 NA NA NA NA NA
## 5348 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 3012 NA Yes
## 5348 NA NA
## [1] "min distance(0.9500) pair:"
## USER_ID Party.fctr Hhold.fctr YOB.Age.fctr Gender.fctr Income.fctr
## 4126 5146 D SKy NA M N
## 6840 6393 <NA> SKy NA M N
## Edn.fctr Q124742.fctr Q124122.fctr Q123621.fctr Q123464.fctr
## 4126 HSD NA NA NA NA
## 6840 N NA NA NA NA
## Q122771.fctr Q122770.fctr Q122769.fctr Q122120.fctr Q121700.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q121699.fctr Q121011.fctr Q120978.fctr Q120650.fctr Q120472.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q120379.fctr Q120194.fctr Q120014.fctr Q120012.fctr Q119851.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q119650.fctr Q119334.fctr Q118892.fctr Q118237.fctr Q118233.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q118232.fctr Q118117.fctr Q117193.fctr Q117186.fctr Q116797.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q116881.fctr Q116953.fctr Q116601.fctr Q116441.fctr Q116448.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA Yes No Yes
## Q116197.fctr Q115602.fctr Q115777.fctr Q115610.fctr Q115611.fctr
## 4126 NA Yes NA NA NA
## 6840 P.M. Yes End Yes Yes
## Q115899.fctr Q115390.fctr Q115195.fctr Q114961.fctr Q114748.fctr
## 4126 NA No No No Yes
## 6840 Cs No Yes Yes Yes
## Q114517.fctr Q114386.fctr Q114152.fctr Q113992.fctr Q113583.fctr
## 4126 Yes NA NA NA NA
## 6840 No Mysterious NA NA NA
## Q113584.fctr Q113181.fctr Q112478.fctr Q112512.fctr Q112270.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q111848.fctr Q111580.fctr Q111220.fctr Q110740.fctr Q109367.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q109244.fctr Q108950.fctr Q108855.fctr Q108617.fctr Q108856.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q108754.fctr Q108342.fctr Q108343.fctr Q107869.fctr Q107491.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q106993.fctr Q106997.fctr Q106272.fctr Q106388.fctr Q106389.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q106042.fctr Q105840.fctr Q105655.fctr Q104996.fctr Q103293.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q102906.fctr Q102674.fctr Q102687.fctr Q102289.fctr Q102089.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q101162.fctr Q101163.fctr Q101596.fctr Q100689.fctr Q100680.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q100562.fctr Q100010.fctr Q99982.fctr Q99716.fctr Q99581.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q99480.fctr Q98869.fctr Q98578.fctr Q98197.fctr Q98059.fctr
## 4126 NA NA NA NA NA
## 6840 NA NA NA NA NA
## Q98078.fctr Q96024.fctr
## 4126 NA NA
## 6840 NA NA
## Hhold.fctr .clusterid Hhold.fctr.clusterid R D .entropy .knt
## 1 N 1 N_1 95 106 0.6916489 201
## 2 N 2 N_2 77 76 0.6931258 153
## 3 N 3 N_3 48 48 0.6931472 96
## 4 MKn 1 MKn_1 205 194 0.6927671 399
## 5 MKn 2 MKn_2 75 89 0.6894991 164
## 6 MKn 3 MKn_3 28 61 0.6227371 89
## 7 MKy 1 MKy_1 620 438 0.6782774 1058
## 8 MKy 2 MKy_2 134 136 0.6931197 270
## 9 MKy 3 MKy_3 88 178 0.6347628 266
## 10 PKn 1 PKn_1 27 69 0.5941300 96
## 11 PKn 2 PKn_2 10 46 0.4692203 56
## 12 PKn 3 PKn_3 12 16 0.6829081 28
## 13 PKy 1 PKy_1 8 9 0.6914161 17
## 14 PKy 2 PKy_2 10 8 0.6869616 18
## 15 PKy 3 PKy_3 7 5 0.6791933 12
## 16 PKy 4 PKy_4 1 13 0.2573186 14
## 17 SKn 1 SKn_1 512 456 0.6914729 968
## 18 SKn 2 SKn_2 257 475 0.6481206 732
## 19 SKn 3 SKn_3 167 200 0.6890991 367
## 20 SKn 4 SKn_4 155 209 0.6821024 364
## 21 SKy 1 SKy_1 45 86 0.6433372 131
## 22 SKy 2 SKy_2 21 17 0.6875967 38
## 23 SKy 3 SKy_3 15 16 0.6926268 31
## [1] "glbObsAll$Hhold.fctr$.clusterid Entropy: 0.6726 (98.0608 pct)"
## label step_major step_minor label_minor bgn
## 1 cluster.data 1 0 0 6.109
## 2 partition.data.training 2 0 0 129.165
## end elapsed
## 1 129.164 123.055
## 2 NA NA
2.0: partition data training## [1] "partition.data.training chunk: setup: elapsed: 0.00 secs"
## Loading required package: sqldf
## Loading required package: gsubfn
## Loading required package: proto
## Loading required package: RSQLite
## Loading required package: DBI
## Loading required package: tcltk
## Loading required package: reshape2
## [1] "partition.data.training chunk: strata_mtrx complete: elapsed: 3.67 secs"
## [1] "partition.data.training chunk: obs_freq_df complete: elapsed: 3.67 secs"
## Loading required package: sampling
##
## Attaching package: 'sampling'
## The following object is masked from 'package:caret':
##
## cluster
## [1] "lclgetMatrixCorrelation: duration: 41.944000 secs"
## [1] "cor of Fit vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 14.834000 secs"
## [1] "cor of New vs. OOB: 1.0000"
## [1] "lclgetMatrixCorrelation: duration: 51.606000 secs"
## [1] "cor of Fit vs. New: 1.0000"
## [1] "partition.data.training chunk: Fit/OOB partition complete: elapsed: 112.71 secs"
## Party.Democrat Party.Republican Party.NA
## NA NA 1392
## Fit 2357 2091 NA
## OOB 594 526 NA
## Party.Democrat Party.Republican Party.NA
## NA NA 1
## Fit 0.5299011 0.4700989 NA
## OOB 0.5303571 0.4696429 NA
## Hhold.fctr .n.Fit .n.OOB .n.Tst .freqRatio.Fit .freqRatio.OOB
## 6 SKn 1920 511 638 0.43165468 0.456250000
## 2 MKy 1296 298 371 0.29136691 0.266071429
## 1 MKn 516 136 169 0.11600719 0.121428571
## 3 N 367 83 102 0.08250899 0.074107143
## 7 SKy 147 53 65 0.03304856 0.047321429
## 4 PKn 150 30 37 0.03372302 0.026785714
## 5 PKy 52 9 10 0.01169065 0.008035714
## .freqRatio.Tst
## 6 0.458333333
## 2 0.266522989
## 1 0.121408046
## 3 0.073275862
## 7 0.046695402
## 4 0.026580460
## 5 0.007183908
## [1] "glbObsAll: "
## [1] 6960 221
## [1] "glbObsTrn: "
## [1] 5568 221
## [1] "glbObsFit: "
## [1] 4448 220
## [1] "glbObsOOB: "
## [1] 1120 220
## [1] "glbObsNew: "
## [1] 1392 220
## [1] "partition.data.training chunk: teardown: elapsed: 113.63 secs"
## label step_major step_minor label_minor bgn
## 2 partition.data.training 2 0 0 129.165
## 3 select.features 3 0 0 242.864
## end elapsed
## 2 242.864 113.699
## 3 NA NA
3.0: select features## cor.y exclude.as.feat cor.y.abs cor.high.X
## Q109244.fctr 0.1203812469 1 0.1203812469 NA
## .clusterid 0.0984277178 1 0.0984277178 NA
## .clusterid.fctr 0.0984277178 0 0.0984277178 NA
## Hhold.fctr 0.0511386673 0 0.0511386673 NA
## Edn.fctr 0.0359295351 1 0.0359295351 NA
## Q101163.fctr 0.0295046473 1 0.0295046473 NA
## Q100689.fctr 0.0256915080 1 0.0256915080 NA
## Q98078.fctr 0.0256516490 1 0.0256516490 NA
## Q99716.fctr 0.0209286674 1 0.0209286674 NA
## Q120379.fctr 0.0206291292 1 0.0206291292 NA
## Q121699.fctr 0.0196933075 1 0.0196933075 NA
## Q105840.fctr 0.0195569165 1 0.0195569165 NA
## Q113583.fctr 0.0191894717 1 0.0191894717 NA
## Q115195.fctr 0.0174522586 1 0.0174522586 NA
## Q102089.fctr 0.0174087944 1 0.0174087944 NA
## Q98059.fctr 0.0171637755 1 0.0171637755 NA
## Q114386.fctr 0.0168013326 1 0.0168013326 NA
## Q100680.fctr 0.0157762454 1 0.0157762454 NA
## Q108342.fctr 0.0151842510 1 0.0151842510 NA
## Q111848.fctr 0.0141099384 1 0.0141099384 NA
## YOB.Age.fctr 0.0129198495 1 0.0129198495 NA
## Q118892.fctr 0.0125250379 1 0.0125250379 NA
## Q102687.fctr 0.0120079165 1 0.0120079165 NA
## Q115390.fctr 0.0119300319 1 0.0119300319 NA
## Q119851.fctr 0.0093381833 1 0.0093381833 NA
## Q114517.fctr 0.0084741753 1 0.0084741753 NA
## Q120012.fctr 0.0084652930 1 0.0084652930 NA
## Q109367.fctr 0.0080456026 1 0.0080456026 NA
## Q114961.fctr 0.0079206587 1 0.0079206587 NA
## Q121700.fctr 0.0067756198 1 0.0067756198 NA
## Q124122.fctr 0.0061257448 1 0.0061257448 NA
## Q111220.fctr 0.0055758571 1 0.0055758571 NA
## Q113992.fctr 0.0041479796 1 0.0041479796 NA
## Q121011.fctr 0.0037329030 1 0.0037329030 NA
## Q106042.fctr 0.0032327194 1 0.0032327194 NA
## Q116448.fctr 0.0031731051 1 0.0031731051 NA
## Q116601.fctr 0.0022379241 1 0.0022379241 NA
## Q104996.fctr 0.0012202806 1 0.0012202806 NA
## Q102906.fctr 0.0011540297 1 0.0011540297 NA
## Q113584.fctr 0.0011387024 1 0.0011387024 NA
## Q108950.fctr 0.0010567028 1 0.0010567028 NA
## Q102674.fctr 0.0009759844 1 0.0009759844 NA
## Q103293.fctr 0.0005915534 1 0.0005915534 NA
## Q112478.fctr 0.0001517248 1 0.0001517248 NA
## Q114748.fctr -0.0008477228 1 0.0008477228 NA
## Q107491.fctr -0.0014031814 1 0.0014031814 NA
## Q100562.fctr -0.0017132769 1 0.0017132769 NA
## Q108617.fctr -0.0024119725 1 0.0024119725 NA
## Q100010.fctr -0.0024291540 1 0.0024291540 NA
## Q115602.fctr -0.0027844465 1 0.0027844465 NA
## Q116953.fctr -0.0029786716 1 0.0029786716 NA
## Q115610.fctr -0.0035255582 1 0.0035255582 NA
## Q106997.fctr -0.0041749086 1 0.0041749086 NA
## Q120978.fctr -0.0044187616 1 0.0044187616 NA
## Q112512.fctr -0.0056768212 1 0.0056768212 NA
## Q108343.fctr -0.0060665340 1 0.0060665340 NA
## Q96024.fctr -0.0069116541 1 0.0069116541 NA
## Q106389.fctr -0.0077498918 1 0.0077498918 NA
## .rnorm -0.0078039520 0 0.0078039520 NA
## Q108754.fctr -0.0080847764 1 0.0080847764 NA
## Q98578.fctr -0.0081164509 1 0.0081164509 NA
## Q101162.fctr -0.0099412952 1 0.0099412952 NA
## Q115777.fctr -0.0101315203 1 0.0101315203 NA
## Q99581.fctr -0.0103662478 1 0.0103662478 NA
## Q124742.fctr -0.0111642906 1 0.0111642906 NA
## Q116797.fctr -0.0112749656 1 0.0112749656 NA
## Q112270.fctr -0.0116157798 1 0.0116157798 NA
## YOB -0.0116828198 1 0.0116828198 NA
## Q118237.fctr -0.0117079669 1 0.0117079669 NA
## Q119650.fctr -0.0125645475 1 0.0125645475 NA
## Q111580.fctr -0.0132382335 1 0.0132382335 NA
## Q123464.fctr -0.0136140083 1 0.0136140083 NA
## Q117193.fctr -0.0138241599 1 0.0138241599 NA
## Q99982.fctr -0.0139727928 1 0.0139727928 NA
## Q108856.fctr -0.0140363785 1 0.0140363785 NA
## Q118233.fctr -0.0147269325 1 0.0147269325 NA
## Q102289.fctr -0.0155850393 1 0.0155850393 NA
## Q116197.fctr -0.0158561766 1 0.0158561766 NA
## Income.fctr -0.0159635458 1 0.0159635458 NA
## Q118232.fctr -0.0171321152 1 0.0171321152 NA
## Q120194.fctr -0.0172986920 1 0.0172986920 NA
## Q114152.fctr -0.0175013163 1 0.0175013163 NA
## Q122770.fctr -0.0194639697 1 0.0194639697 NA
## Q117186.fctr -0.0198853672 1 0.0198853672 NA
## Q105655.fctr -0.0198994078 1 0.0198994078 NA
## Q106993.fctr -0.0207428635 1 0.0207428635 NA
## Q119334.fctr -0.0226894034 1 0.0226894034 NA
## Q122120.fctr -0.0229287700 1 0.0229287700 NA
## Q116441.fctr -0.0237358205 1 0.0237358205 NA
## Q118117.fctr -0.0253544150 1 0.0253544150 NA
## Q123621.fctr -0.0255329743 1 0.0255329743 NA
## Q122769.fctr -0.0259739146 1 0.0259739146 NA
## Q120650.fctr -0.0270889067 1 0.0270889067 NA
## Q98869.fctr -0.0276734114 1 0.0276734114 NA
## .pos -0.0302037138 1 0.0302037138 NA
## USER_ID -0.0302304868 1 0.0302304868 NA
## Q107869.fctr -0.0304661021 1 0.0304661021 NA
## Q120014.fctr -0.0318620439 1 0.0318620439 NA
## Q115899.fctr -0.0324177950 1 0.0324177950 NA
## Q106388.fctr -0.0341579350 1 0.0341579350 NA
## Q99480.fctr -0.0344412239 1 0.0344412239 NA
## Q122771.fctr -0.0348421015 1 0.0348421015 NA
## Q108855.fctr -0.0370970211 1 0.0370970211 NA
## Q110740.fctr -0.0380691243 1 0.0380691243 NA
## Q106272.fctr -0.0400926462 1 0.0400926462 NA
## Q101596.fctr -0.0409784077 1 0.0409784077 NA
## Q116881.fctr -0.0416860293 1 0.0416860293 NA
## Q120472.fctr -0.0462030674 1 0.0462030674 NA
## Q98197.fctr -0.0549342527 1 0.0549342527 NA
## Q113181.fctr -0.0808753072 1 0.0808753072 NA
## Q115611.fctr -0.0904468203 1 0.0904468203 NA
## Gender.fctr -0.1027400851 1 0.1027400851 NA
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## Q109244.fctr 1.125916 0.05387931 FALSE FALSE FALSE
## .clusterid 2.005590 0.07183908 FALSE FALSE FALSE
## .clusterid.fctr 2.005590 0.07183908 FALSE FALSE FALSE
## Hhold.fctr 1.525094 0.12571839 FALSE FALSE FALSE
## Edn.fctr 1.392610 0.14367816 FALSE FALSE FALSE
## Q101163.fctr 1.327394 0.05387931 FALSE FALSE FALSE
## Q100689.fctr 1.029800 0.05387931 FALSE FALSE FALSE
## Q98078.fctr 1.266595 0.05387931 FALSE FALSE FALSE
## Q99716.fctr 1.328693 0.05387931 FALSE FALSE FALSE
## Q120379.fctr 1.046326 0.05387931 FALSE FALSE FALSE
## Q121699.fctr 1.507127 0.05387931 FALSE FALSE FALSE
## Q105840.fctr 1.275362 0.05387931 FALSE FALSE FALSE
## Q113583.fctr 1.102515 0.05387931 FALSE FALSE FALSE
## Q115195.fctr 1.065496 0.05387931 FALSE FALSE FALSE
## Q102089.fctr 1.055963 0.05387931 FALSE FALSE FALSE
## Q98059.fctr 1.493810 0.05387931 FALSE FALSE FALSE
## Q114386.fctr 1.092072 0.05387931 FALSE FALSE FALSE
## Q100680.fctr 1.102386 0.05387931 FALSE FALSE FALSE
## Q108342.fctr 1.048292 0.05387931 FALSE FALSE FALSE
## Q111848.fctr 1.113602 0.05387931 FALSE FALSE FALSE
## YOB.Age.fctr 1.005794 0.16163793 FALSE FALSE FALSE
## Q118892.fctr 1.347380 0.05387931 FALSE FALSE FALSE
## Q102687.fctr 1.256545 0.05387931 FALSE FALSE FALSE
## Q115390.fctr 1.150505 0.05387931 FALSE FALSE FALSE
## Q119851.fctr 1.244519 0.05387931 FALSE FALSE FALSE
## Q114517.fctr 1.183374 0.05387931 FALSE FALSE FALSE
## Q120012.fctr 1.047185 0.05387931 FALSE FALSE FALSE
## Q109367.fctr 1.008571 0.05387931 FALSE FALSE FALSE
## Q114961.fctr 1.250436 0.05387931 FALSE FALSE FALSE
## Q121700.fctr 1.708221 0.05387931 FALSE FALSE TRUE
## Q124122.fctr 1.412807 0.05387931 FALSE FALSE TRUE
## Q111220.fctr 1.262849 0.05387931 FALSE FALSE TRUE
## Q113992.fctr 1.267442 0.05387931 FALSE FALSE TRUE
## Q121011.fctr 1.153676 0.05387931 FALSE FALSE TRUE
## Q106042.fctr 1.247738 0.05387931 FALSE FALSE TRUE
## Q116448.fctr 1.161031 0.05387931 FALSE FALSE TRUE
## Q116601.fctr 1.394914 0.05387931 FALSE FALSE TRUE
## Q104996.fctr 1.173840 0.05387931 FALSE FALSE TRUE
## Q102906.fctr 1.053396 0.05387931 FALSE FALSE TRUE
## Q113584.fctr 1.212486 0.05387931 FALSE FALSE TRUE
## Q108950.fctr 1.103872 0.05387931 FALSE FALSE TRUE
## Q102674.fctr 1.073412 0.05387931 FALSE FALSE TRUE
## Q103293.fctr 1.122287 0.05387931 FALSE FALSE TRUE
## Q112478.fctr 1.113648 0.05387931 FALSE FALSE TRUE
## Q114748.fctr 1.051125 0.05387931 FALSE FALSE TRUE
## Q107491.fctr 1.419021 0.05387931 FALSE FALSE TRUE
## Q100562.fctr 1.217215 0.05387931 FALSE FALSE TRUE
## Q108617.fctr 1.390618 0.05387931 FALSE FALSE TRUE
## Q100010.fctr 1.268156 0.05387931 FALSE FALSE TRUE
## Q115602.fctr 1.322302 0.05387931 FALSE FALSE TRUE
## Q116953.fctr 1.039180 0.05387931 FALSE FALSE TRUE
## Q115610.fctr 1.359695 0.05387931 FALSE FALSE TRUE
## Q106997.fctr 1.177632 0.05387931 FALSE FALSE TRUE
## Q120978.fctr 1.131963 0.05387931 FALSE FALSE TRUE
## Q112512.fctr 1.299253 0.05387931 FALSE FALSE TRUE
## Q108343.fctr 1.064910 0.05387931 FALSE FALSE TRUE
## Q96024.fctr 1.144428 0.05387931 FALSE FALSE TRUE
## Q106389.fctr 1.341307 0.05387931 FALSE FALSE TRUE
## .rnorm 1.000000 100.00000000 FALSE FALSE FALSE
## Q108754.fctr 1.008090 0.05387931 FALSE FALSE FALSE
## Q98578.fctr 1.093556 0.05387931 FALSE FALSE FALSE
## Q101162.fctr 1.103229 0.05387931 FALSE FALSE FALSE
## Q115777.fctr 1.140288 0.05387931 FALSE FALSE FALSE
## Q99581.fctr 1.375000 0.05387931 FALSE FALSE FALSE
## Q124742.fctr 2.565379 0.05387931 FALSE FALSE FALSE
## Q116797.fctr 1.009589 0.05387931 FALSE FALSE FALSE
## Q112270.fctr 1.254284 0.05387931 FALSE FALSE FALSE
## YOB 1.027559 1.41882184 FALSE FALSE FALSE
## Q118237.fctr 1.088017 0.05387931 FALSE FALSE FALSE
## Q119650.fctr 1.456978 0.05387931 FALSE FALSE FALSE
## Q111580.fctr 1.024977 0.05387931 FALSE FALSE FALSE
## Q123464.fctr 1.326681 0.05387931 FALSE FALSE FALSE
## Q117193.fctr 1.140665 0.05387931 FALSE FALSE FALSE
## Q99982.fctr 1.339380 0.05387931 FALSE FALSE FALSE
## Q108856.fctr 1.080645 0.05387931 FALSE FALSE FALSE
## Q118233.fctr 1.199142 0.05387931 FALSE FALSE FALSE
## Q102289.fctr 1.033482 0.05387931 FALSE FALSE FALSE
## Q116197.fctr 1.073778 0.05387931 FALSE FALSE FALSE
## Income.fctr 1.256724 0.12571839 FALSE FALSE FALSE
## Q118232.fctr 1.365812 0.05387931 FALSE FALSE FALSE
## Q120194.fctr 1.016716 0.05387931 FALSE FALSE FALSE
## Q114152.fctr 1.027617 0.05387931 FALSE FALSE FALSE
## Q122770.fctr 1.008802 0.05387931 FALSE FALSE FALSE
## Q117186.fctr 1.053878 0.05387931 FALSE FALSE FALSE
## Q105655.fctr 1.079316 0.05387931 FALSE FALSE FALSE
## Q106993.fctr 1.327392 0.05387931 FALSE FALSE FALSE
## Q119334.fctr 1.081498 0.05387931 FALSE FALSE FALSE
## Q122120.fctr 1.297443 0.05387931 FALSE FALSE FALSE
## Q116441.fctr 1.019645 0.05387931 FALSE FALSE FALSE
## Q118117.fctr 1.174006 0.05387931 FALSE FALSE FALSE
## Q123621.fctr 1.466381 0.05387931 FALSE FALSE FALSE
## Q122769.fctr 1.060606 0.05387931 FALSE FALSE FALSE
## Q120650.fctr 1.896247 0.05387931 FALSE FALSE FALSE
## Q98869.fctr 1.080860 0.05387931 FALSE FALSE FALSE
## .pos 1.000000 100.00000000 FALSE FALSE FALSE
## USER_ID 1.000000 100.00000000 FALSE FALSE FALSE
## Q107869.fctr 1.211050 0.05387931 FALSE FALSE FALSE
## Q120014.fctr 1.044944 0.05387931 FALSE FALSE FALSE
## Q115899.fctr 1.197849 0.05387931 FALSE FALSE FALSE
## Q106388.fctr 1.065033 0.05387931 FALSE FALSE FALSE
## Q99480.fctr 1.225404 0.05387931 FALSE FALSE FALSE
## Q122771.fctr 1.414753 0.05387931 FALSE FALSE FALSE
## Q108855.fctr 1.273980 0.05387931 FALSE FALSE FALSE
## Q110740.fctr 1.050779 0.05387931 FALSE FALSE FALSE
## Q106272.fctr 1.116536 0.05387931 FALSE FALSE FALSE
## Q101596.fctr 1.041667 0.05387931 FALSE FALSE FALSE
## Q116881.fctr 1.010066 0.05387931 FALSE FALSE FALSE
## Q120472.fctr 1.292633 0.05387931 FALSE FALSE FALSE
## Q98197.fctr 1.129371 0.05387931 FALSE FALSE FALSE
## Q113181.fctr 1.006354 0.05387931 FALSE FALSE FALSE
## Q115611.fctr 1.194859 0.05387931 FALSE FALSE FALSE
## Gender.fctr 1.561033 0.05387931 FALSE FALSE FALSE
## Warning in myplot_scatter(plt_feats_df, "percentUnique", "freqRatio",
## colorcol_name = "nzv", : converting nzv to class:factor
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## Warning: Removed 3 rows containing missing values (geom_point).
## [1] cor.y exclude.as.feat cor.y.abs cor.high.X
## [5] freqRatio percentUnique zeroVar nzv
## [9] is.cor.y.abs.low
## <0 rows> (or 0-length row.names)
## Scale for 'y' is already present. Adding another scale for 'y', which
## will replace the existing scale.
## [1] "numeric data missing in glbObsAll: "
## YOB Party.fctr
## 415 1392
## [1] "numeric data w/ 0s in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ Infs in glbObsAll: "
## named integer(0)
## [1] "numeric data w/ NaNs in glbObsAll: "
## named integer(0)
## [1] "string data missing in glbObsAll: "
## Gender Income HouseholdStatus EducationLevel
## 143 1273 552 1067
## Party Q124742 Q124122 Q123464
## NA 4340 3114 2912
## Q123621 Q122769 Q122770 Q122771
## 3018 2778 2597 2579
## Q122120 Q121699 Q121700 Q120978
## 2552 2279 2328 2303
## Q121011 Q120379 Q120650 Q120472
## 2256 2361 2283 2433
## Q120194 Q120012 Q120014 Q119334
## 2603 2344 2571 2477
## Q119851 Q119650 Q118892 Q118117
## 2243 2374 2206 2342
## Q118232 Q118233 Q118237 Q117186
## 3018 2659 2592 2845
## Q117193 Q116797 Q116881 Q116953
## 2799 2771 2889 2848
## Q116601 Q116441 Q116448 Q116197
## 2606 2684 2730 2657
## Q115602 Q115777 Q115610 Q115611
## 2619 2785 2637 2443
## Q115899 Q115390 Q114961 Q114748
## 2789 2860 2687 2462
## Q115195 Q114517 Q114386 Q113992
## 2647 2567 2686 2502
## Q114152 Q113583 Q113584 Q113181
## 2829 2632 2654 2576
## Q112478 Q112512 Q112270 Q111848
## 2790 2676 2820 2449
## Q111580 Q111220 Q110740 Q109367
## 2686 2563 2479 2624
## Q108950 Q109244 Q108855 Q108617
## 2641 2731 3008 2696
## Q108856 Q108754 Q108342 Q108343
## 3007 2770 2760 2736
## Q107869 Q107491 Q106993 Q106997
## 2762 2667 2676 2702
## Q106272 Q106388 Q106389 Q106042
## 2722 2818 2871 2762
## Q105840 Q105655 Q104996 Q103293
## 2876 2612 2620 2674
## Q102906 Q102674 Q102687 Q102289
## 2840 2864 2712 2790
## Q102089 Q101162 Q101163 Q101596
## 2736 2816 2995 2824
## Q100689 Q100680 Q100562 Q99982
## 2568 2787 2793 2871
## Q100010 Q99716 Q99581 Q99480
## 2688 2790 2690 2700
## Q98869 Q98578 Q98059 Q98078
## 2906 2867 2629 2945
## Q98197 Q96024 .lcn
## 2836 2858 1392
## [1] "glb_feats_df:"
## [1] 112 12
## id exclude.as.feat rsp_var
## Party.fctr Party.fctr TRUE TRUE
## id cor.y exclude.as.feat cor.y.abs cor.high.X
## USER_ID USER_ID -0.03023049 TRUE 0.03023049 NA
## Party.fctr Party.fctr NA TRUE NA NA
## freqRatio percentUnique zeroVar nzv is.cor.y.abs.low
## USER_ID 1 100 FALSE FALSE FALSE
## Party.fctr NA NA NA NA NA
## interaction.feat shapiro.test.p.value rsp_var_raw id_var
## USER_ID <NA> NA FALSE TRUE
## Party.fctr <NA> NA NA NA
## rsp_var
## USER_ID NA
## Party.fctr TRUE
## [1] "glb_feats_df vs. glbObsAll: "
## character(0)
## [1] "glbObsAll vs. glb_feats_df: "
## character(0)
## label step_major step_minor label_minor bgn end
## 3 select.features 3 0 0 242.864 245.735
## 4 fit.models 4 0 0 245.736 NA
## elapsed
## 3 2.871
## 4 NA
4.0: fit modelsfit.models_0_chunk_df <- myadd_chunk(NULL, "fit.models_0_bgn", label.minor = "setup")
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_0_bgn 1 0 setup 246.296 NA NA
# load(paste0(glbOut$pfx, "dsk.RData"))
get_model_sel_frmla <- function() {
model_evl_terms <- c(NULL)
# min.aic.fit might not be avl
lclMdlEvlCriteria <-
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)]
for (metric in lclMdlEvlCriteria)
model_evl_terms <- c(model_evl_terms,
ifelse(length(grep("max", metric)) > 0, "-", "+"), metric)
if (glb_is_classification && glb_is_binomial)
model_evl_terms <- c(model_evl_terms, "-", "opt.prob.threshold.OOB")
model_sel_frmla <- as.formula(paste(c("~ ", model_evl_terms), collapse = " "))
return(model_sel_frmla)
}
get_dsp_models_df <- function() {
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
dsp_models_df <-
#orderBy(get_model_sel_frmla(), glb_models_df)[, c("id", glbMdlMetricsEval)]
orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols]
nCvMdl <- sapply(glb_models_lst, function(mdl) nrow(mdl$results))
nParams <- sapply(glb_models_lst, function(mdl) ifelse(mdl$method == "custom", 0,
nrow(subset(modelLookup(mdl$method), parameter != "parameter"))))
# nCvMdl <- nCvMdl[names(nCvMdl) != "avNNet"]
# nParams <- nParams[names(nParams) != "avNNet"]
if (length(cvMdlProblems <- nCvMdl[nCvMdl <= nParams]) > 0) {
print("Cross Validation issues:")
warning("Cross Validation issues:")
print(cvMdlProblems)
}
pltMdls <- setdiff(names(nCvMdl), names(cvMdlProblems))
pltMdls <- setdiff(pltMdls, names(nParams[nParams == 0]))
# length(pltMdls) == 21
png(paste0(glbOut$pfx, "bestTune.png"), width = 480 * 2, height = 480 * 4)
grid.newpage()
pushViewport(viewport(layout = grid.layout(ceiling(length(pltMdls) / 2.0), 2)))
pltIx <- 1
for (mdlId in pltMdls) {
print(ggplot(glb_models_lst[[mdlId]], highBestTune = TRUE) + labs(title = mdlId),
vp = viewport(layout.pos.row = ceiling(pltIx / 2.0),
layout.pos.col = ((pltIx - 1) %% 2) + 1))
pltIx <- pltIx + 1
}
dev.off()
if (all(row.names(dsp_models_df) != dsp_models_df$id))
row.names(dsp_models_df) <- dsp_models_df$id
return(dsp_models_df)
}
#get_dsp_models_df()
if (glb_is_classification && glb_is_binomial &&
(length(unique(glbObsFit[, glb_rsp_var])) < 2))
stop("glbObsFit$", glb_rsp_var, ": contains less than 2 unique values: ",
paste0(unique(glbObsFit[, glb_rsp_var]), collapse=", "))
max_cor_y_x_vars <- orderBy(~ -cor.y.abs,
subset(glb_feats_df, (exclude.as.feat == 0) & !nzv & !is.cor.y.abs.low &
is.na(cor.high.X)))[1:2, "id"]
max_cor_y_x_vars <- max_cor_y_x_vars[!is.na(max_cor_y_x_vars)]
if (length(max_cor_y_x_vars) < 2)
max_cor_y_x_vars <- union(max_cor_y_x_vars, ".pos")
if (!is.null(glb_Baseline_mdl_var)) {
if ((max_cor_y_x_vars[1] != glb_Baseline_mdl_var) &
(glb_feats_df[glb_feats_df$id == max_cor_y_x_vars[1], "cor.y.abs"] >
glb_feats_df[glb_feats_df$id == glb_Baseline_mdl_var, "cor.y.abs"]))
stop(max_cor_y_x_vars[1], " has a higher correlation with ", glb_rsp_var,
" than the Baseline var: ", glb_Baseline_mdl_var)
}
glb_model_type <- ifelse(glb_is_regression, "regression", "classification")
# Model specs
# c("id.prefix", "method", "type",
# # trainControl params
# "preProc.method", "cv.n.folds", "cv.n.repeats", "summary.fn",
# # train params
# "metric", "metric.maximize", "tune.df")
# Baseline
if (!is.null(glb_Baseline_mdl_var)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Baseline"), major.inc = FALSE,
label.minor = "mybaseln_classfr")
ret_lst <- myfit_mdl(mdl_id="Baseline",
model_method="mybaseln_classfr",
indepVar=glb_Baseline_mdl_var,
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Most Frequent Outcome "MFO" model: mean(y) for regression
# Not using caret's nullModel since model stats not avl
# Cannot use rpart for multinomial classification since it predicts non-MFO
if (glb_is_classification) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "MFO"), major.inc = FALSE,
label.minor = "myMFO_classfr")
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "MFO", type = glb_model_type, trainControl.method = "none",
train.method = ifelse(glb_is_regression, "lm", "myMFO_classfr"))),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
# "random" model - only for classification;
# none needed for regression since it is same as MFO
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Random"), major.inc = FALSE,
label.minor = "myrandom_classfr")
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Random", type = glb_model_type, trainControl.method = "none",
train.method = "myrandom_classfr")),
indepVar = ".rnorm", rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
## label step_major step_minor label_minor bgn end
## 1 fit.models_0_bgn 1 0 setup 246.296 246.328
## 2 fit.models_0_MFO 1 1 myMFO_classfr 246.329 NA
## elapsed
## 1 0.032
## 2 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: MFO###myMFO_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.484000 secs"
## Fitting parameter = none on full training set
## [1] "in MFO.Classifier$fit"
## [1] "unique.vals:"
## [1] R D
## Levels: R D
## [1] "unique.prob:"
## y
## D R
## 0.5299011 0.4700989
## [1] "MFO.val:"
## [1] "D"
## [1] "myfit_mdl: train complete: 0.986000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 -none- numeric
## MFO.val 1 -none- character
## x.names 1 -none- character
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.988000 secs"
## Loading required namespace: pROC
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## Loading required package: ROCR
## Loading required package: gplots
##
## Attaching package: 'gplots'
## The following object is masked from 'package:stats':
##
## lowess
## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "entr MFO.Classifier$predict"
## [1] "exit MFO.Classifier$predict"
## [1] "in MFO.Classifier$prob"
## R D
## 1 0.5299011 0.4700989
## 2 0.5299011 0.4700989
## 3 0.5299011 0.4700989
## 4 0.5299011 0.4700989
## 5 0.5299011 0.4700989
## 6 0.5299011 0.4700989
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 5.900000 secs"
## id feats max.nTuningRuns min.elapsedtime.everything
## 1 MFO###myMFO_classfr .rnorm 0 0.491
## min.elapsedtime.final max.AUCpROC.fit max.Sens.fit max.Spec.fit
## 1 0.004 0.5 0 1
## max.AUCROCR.fit opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.5 0.5 0.6395473 0.4700989
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4553427 0.4848945 0
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5 0 1 0.5
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.5 0.6391252 0.4696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4400805 0.4993651 0
## [1] "myfit_mdl: exit: 5.910000 secs"
## label step_major step_minor label_minor bgn
## 2 fit.models_0_MFO 1 1 myMFO_classfr 246.329
## 3 fit.models_0_Random 1 2 myrandom_classfr 252.245
## end elapsed
## 2 252.244 5.915
## 3 NA NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Random###myrandom_classfr"
## [1] " indepVar: .rnorm"
## [1] "myfit_mdl: setup complete: 0.436000 secs"
## Fitting parameter = none on full training set
## [1] "myfit_mdl: train complete: 0.735000 secs"
## Length Class Mode
## unique.vals 2 factor numeric
## unique.prob 2 table numeric
## xNames 1 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## [1] "myfit_mdl: train diagnostics complete: 0.736000 secs"
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 2091 0
## D 2357 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4700989 0.0000000 0.4553427 0.4848945 0.5299011
## AccuracyPValue McnemarPValue
## 1.0000000 0.0000000
## [1] "in Random.Classifier$prob"
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 6.783000 secs"
## id feats max.nTuningRuns
## 1 Random###myrandom_classfr .rnorm 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.291 0.003 0.4942483
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.4619799 0.5265168 0.5073101 0.55
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6395473 0.4700989 0.4553427
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.4848945 0 0.523569 0.5
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.547138 0.5191202 0.55 0.6391252
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4696429 0.4400805 0.4993651
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 6.796000 secs"
# Max.cor.Y
# Check impact of cv
# rpart is not a good candidate since caret does not optimize cp (only tuning parameter of rpart) well
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.rcv.*X*"), major.inc = FALSE,
label.minor = "glmnet")
## label step_major step_minor label_minor
## 3 fit.models_0_Random 1 2 myrandom_classfr
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet
## bgn end elapsed
## 3 252.245 259.052 6.807
## 4 259.053 NA NA
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.rcv.1X1", type = glb_model_type, trainControl.method = "none",
train.method = "glmnet")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y.rcv.1X1###glmnet"
## [1] " indepVar: .clusterid.fctr,Hhold.fctr"
## [1] "myfit_mdl: setup complete: 0.695000 secs"
## Loading required package: glmnet
## Loading required package: Matrix
##
## Attaching package: 'Matrix'
## The following object is masked from 'package:tidyr':
##
## expand
## Loaded glmnet 2.0-5
## Fitting alpha = 0.1, lambda = 0.000918 on full training set
## [1] "myfit_mdl: train complete: 1.528000 secs"
## Length Class Mode
## a0 51 -none- numeric
## beta 459 dgCMatrix S4
## df 51 -none- numeric
## dim 2 -none- numeric
## lambda 51 -none- numeric
## dev.ratio 51 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 9 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) .clusterid.fctr2 .clusterid.fctr3 .clusterid.fctr4
## -0.1892506 0.4329138 0.4635477 0.4269151
## Hhold.fctrMKn Hhold.fctrMKy Hhold.fctrPKn Hhold.fctrPKy
## 0.1201887 -0.1244717 1.1421040 0.1608926
## Hhold.fctrSKn Hhold.fctrSKy
## 0.1515067 0.5006923
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".clusterid.fctr2" ".clusterid.fctr3"
## [4] ".clusterid.fctr4" "Hhold.fctrMKn" "Hhold.fctrMKy"
## [7] "Hhold.fctrPKn" "Hhold.fctrPKy" "Hhold.fctrSKn"
## [10] "Hhold.fctrSKy"
## [1] "myfit_mdl: train diagnostics complete: 1.639000 secs"
## Prediction
## Reference R D
## R 2056 35
## D 2242 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.48808453 0.03025076 0.47329434 0.50289038 0.52990108
## AccuracyPValue McnemarPValue
## 0.99999999 0.00000000
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 6.107000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y.rcv.1X1###glmnet .clusterid.fctr,Hhold.fctr 0
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 0.823 0.074 0.5754816
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.555715 0.5952482 0.4065966 0.7
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6436062 0.4880845 0.4732943
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5028904 0.03025076 0.5546626 0.5285171
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5808081 0.4525163 0.85 0.6391252
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4696429 0.4400805 0.4993651
## max.Kappa.OOB
## 1 0
## [1] "myfit_mdl: exit: 6.120000 secs"
if (glbMdlCheckRcv) {
# rcv_n_folds == 1 & rcv_n_repeats > 1 crashes
for (rcv_n_folds in seq(3, glb_rcv_n_folds + 2, 2))
for (rcv_n_repeats in seq(1, glb_rcv_n_repeats + 2, 2)) {
# Experiment specific code to avoid caret crash
# lcl_tune_models_df <- rbind(data.frame()
# ,data.frame(method = "glmnet", parameter = "alpha",
# vals = "0.100 0.325 0.550 0.775 1.000")
# ,data.frame(method = "glmnet", parameter = "lambda",
# vals = "9.342e-02")
# )
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst =
list(
id.prefix = paste0("Max.cor.Y.rcv.", rcv_n_folds, "X", rcv_n_repeats),
type = glb_model_type,
# tune.df = lcl_tune_models_df,
trainControl.method = "repeatedcv",
trainControl.number = rcv_n_folds,
trainControl.repeats = rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.method = "glmnet", train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize)),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Add parallel coordinates graph of glb_models_df[, glbMdlMetricsEval] to evaluate cv parameters
tmp_models_cols <- c("id", "max.nTuningRuns",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
print(myplot_parcoord(obs_df = subset(glb_models_df,
grepl("Max.cor.Y.rcv.", id, fixed = TRUE),
select = -feats)[, tmp_models_cols],
id_var = "id"))
}
# Useful for stacking decisions
# fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
# paste0("fit.models_0_", "Max.cor.Y[rcv.1X1.cp.0|]"), major.inc = FALSE,
# label.minor = "rpart")
#
# ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
# id.prefix = "Max.cor.Y.rcv.1X1.cp.0", type = glb_model_type, trainControl.method = "none",
# train.method = "rpart",
# tune.df=data.frame(method="rpart", parameter="cp", min=0.0, max=0.0, by=0.1))),
# indepVar=max_cor_y_x_vars, rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB)
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
# if (glb_is_regression || glb_is_binomial) # For multinomials this model will be run next by default
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "rpart")),
indepVar = max_cor_y_x_vars, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Max.cor.Y##rcv#rpart"
## [1] " indepVar: .clusterid.fctr,Hhold.fctr"
## [1] "myfit_mdl: setup complete: 0.717000 secs"
## Loading required package: rpart
## Aggregating results
## Selecting tuning parameters
## Fitting cp = 0.000717 on full training set
## [1] "myfit_mdl: train complete: 2.496000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Max.cor.Y", : model's bestTune found at an extreme of
## tuneGrid for parameter: cp
## Loading required package: rpart.plot
## Call:
## rpart(formula = .outcome ~ ., control = list(minsplit = 20, minbucket = 7,
## cp = 0, maxcompete = 4, maxsurrogate = 5, usesurrogate = 2,
## surrogatestyle = 0, maxdepth = 30, xval = 0))
## n= 4448
##
## CP nsplit rel error
## 1 0.0516499283 0 1.0000000
## 2 0.0291726447 1 0.9483501
## 3 0.0042085127 2 0.9191774
## 4 0.0023912004 7 0.8981349
## 5 0.0007173601 9 0.8933525
##
## Variable importance
## .clusterid.fctr3 Hhold.fctrMKy Hhold.fctrPKn Hhold.fctrSKn
## 25 21 15 14
## .clusterid.fctr2 Hhold.fctrSKy Hhold.fctrMKn .clusterid.fctr4
## 8 6 6 5
## Hhold.fctrPKy
## 1
##
## Node number 1: 4448 observations, complexity param=0.05164993
## predicted class=D expected loss=0.4700989 P(node) =1
## class counts: 2091 2357
## probabilities: 0.470 0.530
## left son=2 (1296 obs) right son=3 (3152 obs)
## Primary splits:
## Hhold.fctrMKy < 0.5 to the right, improve=18.734760, (0 missing)
## Hhold.fctrPKn < 0.5 to the left, improve=17.404310, (0 missing)
## .clusterid.fctr2 < 0.5 to the left, improve=11.862420, (0 missing)
## .clusterid.fctr3 < 0.5 to the left, improve= 6.520434, (0 missing)
## Hhold.fctrSKn < 0.5 to the left, improve= 4.878051, (0 missing)
## Surrogate splits:
## Hhold.fctrSKn < 0.5 to the left, agree=0.723, adj=0.049, (0 split)
##
## Node number 2: 1296 observations, complexity param=0.02917264
## predicted class=R expected loss=0.4583333 P(node) =0.2913669
## class counts: 702 594
## probabilities: 0.542 0.458
## left son=4 (1087 obs) right son=5 (209 obs)
## Primary splits:
## .clusterid.fctr3 < 0.5 to the left, improve=17.5394500, (0 missing)
## .clusterid.fctr2 < 0.5 to the left, improve= 0.4996698, (0 missing)
##
## Node number 3: 3152 observations, complexity param=0.004208513
## predicted class=D expected loss=0.4406726 P(node) =0.7086331
## class counts: 1389 1763
## probabilities: 0.441 0.559
## left son=6 (3002 obs) right son=7 (150 obs)
## Primary splits:
## Hhold.fctrPKn < 0.5 to the left, improve=13.541280, (0 missing)
## .clusterid.fctr2 < 0.5 to the left, improve= 8.671724, (0 missing)
## Hhold.fctrMKn < 0.5 to the right, improve= 1.279129, (0 missing)
## Hhold.fctrSKy < 0.5 to the left, improve= 1.099846, (0 missing)
## .clusterid.fctr4 < 0.5 to the left, improve= 1.089559, (0 missing)
##
## Node number 4: 1087 observations
## predicted class=R expected loss=0.4222631 P(node) =0.2443795
## class counts: 628 459
## probabilities: 0.578 0.422
##
## Node number 5: 209 observations
## predicted class=D expected loss=0.354067 P(node) =0.04698741
## class counts: 74 135
## probabilities: 0.354 0.646
##
## Node number 6: 3002 observations, complexity param=0.004208513
## predicted class=D expected loss=0.4510326 P(node) =0.6749101
## class counts: 1354 1648
## probabilities: 0.451 0.549
## left son=12 (2136 obs) right son=13 (866 obs)
## Primary splits:
## .clusterid.fctr2 < 0.5 to the left, improve=7.0467130, (0 missing)
## .clusterid.fctr4 < 0.5 to the left, improve=1.7360290, (0 missing)
## Hhold.fctrSKy < 0.5 to the left, improve=1.5182500, (0 missing)
## Hhold.fctrSKn < 0.5 to the left, improve=0.6487705, (0 missing)
## Hhold.fctrMKn < 0.5 to the right, improve=0.5941805, (0 missing)
##
## Node number 7: 150 observations
## predicted class=D expected loss=0.2333333 P(node) =0.03372302
## class counts: 35 115
## probabilities: 0.233 0.767
##
## Node number 12: 2136 observations, complexity param=0.004208513
## predicted class=D expected loss=0.4728464 P(node) =0.4802158
## class counts: 1010 1126
## probabilities: 0.473 0.527
## left son=24 (2015 obs) right son=25 (121 obs)
## Primary splits:
## Hhold.fctrSKy < 0.5 to the left, improve=4.6065270, (0 missing)
## .clusterid.fctr4 < 0.5 to the left, improve=3.7374020, (0 missing)
## .clusterid.fctr3 < 0.5 to the left, improve=1.8085500, (0 missing)
## Hhold.fctrSKn < 0.5 to the right, improve=1.4825360, (0 missing)
## Hhold.fctrPKy < 0.5 to the left, improve=0.8437651, (0 missing)
##
## Node number 13: 866 observations, complexity param=0.0023912
## predicted class=D expected loss=0.3972286 P(node) =0.1946942
## class counts: 344 522
## probabilities: 0.397 0.603
## left son=26 (297 obs) right son=27 (569 obs)
## Primary splits:
## Hhold.fctrSKn < 0.5 to the left, improve=11.1767300, (0 missing)
## Hhold.fctrMKn < 0.5 to the right, improve= 2.0017040, (0 missing)
## Hhold.fctrSKy < 0.5 to the right, improve= 1.7310560, (0 missing)
## Hhold.fctrPKy < 0.5 to the right, improve= 0.8636391, (0 missing)
## Surrogate splits:
## Hhold.fctrMKn < 0.5 to the right, agree=0.801, adj=0.421, (0 split)
## Hhold.fctrSKy < 0.5 to the right, agree=0.687, adj=0.088, (0 split)
## Hhold.fctrPKy < 0.5 to the right, agree=0.673, adj=0.047, (0 split)
##
## Node number 24: 2015 observations, complexity param=0.004208513
## predicted class=D expected loss=0.4808933 P(node) =0.4530126
## class counts: 969 1046
## probabilities: 0.481 0.519
## left son=48 (1708 obs) right son=49 (307 obs)
## Primary splits:
## .clusterid.fctr4 < 0.5 to the left, improve=4.6639860, (0 missing)
## .clusterid.fctr3 < 0.5 to the left, improve=2.5688400, (0 missing)
## Hhold.fctrPKy < 0.5 to the left, improve=0.9798796, (0 missing)
## Hhold.fctrSKn < 0.5 to the right, improve=0.3104652, (0 missing)
## Hhold.fctrMKn < 0.5 to the left, improve=0.1030380, (0 missing)
##
## Node number 25: 121 observations
## predicted class=D expected loss=0.338843 P(node) =0.02720324
## class counts: 41 80
## probabilities: 0.339 0.661
##
## Node number 26: 297 observations, complexity param=0.0023912
## predicted class=R expected loss=0.4915825 P(node) =0.06677158
## class counts: 151 146
## probabilities: 0.508 0.492
## left son=52 (172 obs) right son=53 (125 obs)
## Primary splits:
## Hhold.fctrMKn < 0.5 to the left, improve=0.3486101, (0 missing)
## Hhold.fctrSKy < 0.5 to the right, improve=0.2674498, (0 missing)
## Hhold.fctrPKy < 0.5 to the right, improve=0.1166707, (0 missing)
##
## Node number 27: 569 observations
## predicted class=D expected loss=0.3391916 P(node) =0.1279227
## class counts: 193 376
## probabilities: 0.339 0.661
##
## Node number 48: 1708 observations, complexity param=0.004208513
## predicted class=D expected loss=0.4953162 P(node) =0.3839928
## class counts: 846 862
## probabilities: 0.495 0.505
## left son=96 (1252 obs) right son=97 (456 obs)
## Primary splits:
## .clusterid.fctr3 < 0.5 to the left, improve=4.64559000, (0 missing)
## Hhold.fctrSKn < 0.5 to the right, improve=0.76958710, (0 missing)
## Hhold.fctrMKn < 0.5 to the left, improve=0.62013200, (0 missing)
## Hhold.fctrPKy < 0.5 to the right, improve=0.03091713, (0 missing)
##
## Node number 49: 307 observations
## predicted class=D expected loss=0.4006515 P(node) =0.06901978
## class counts: 123 184
## probabilities: 0.401 0.599
##
## Node number 52: 172 observations
## predicted class=R expected loss=0.4709302 P(node) =0.03866906
## class counts: 91 81
## probabilities: 0.529 0.471
##
## Node number 53: 125 observations
## predicted class=D expected loss=0.48 P(node) =0.02810252
## class counts: 60 65
## probabilities: 0.480 0.520
##
## Node number 96: 1252 observations
## predicted class=R expected loss=0.4824281 P(node) =0.2814748
## class counts: 648 604
## probabilities: 0.518 0.482
##
## Node number 97: 456 observations
## predicted class=D expected loss=0.4342105 P(node) =0.102518
## class counts: 198 258
## probabilities: 0.434 0.566
##
## n= 4448
##
## node), split, n, loss, yval, (yprob)
## * denotes terminal node
##
## 1) root 4448 2091 D (0.4700989 0.5299011)
## 2) Hhold.fctrMKy>=0.5 1296 594 R (0.5416667 0.4583333)
## 4) .clusterid.fctr3< 0.5 1087 459 R (0.5777369 0.4222631) *
## 5) .clusterid.fctr3>=0.5 209 74 D (0.3540670 0.6459330) *
## 3) Hhold.fctrMKy< 0.5 3152 1389 D (0.4406726 0.5593274)
## 6) Hhold.fctrPKn< 0.5 3002 1354 D (0.4510326 0.5489674)
## 12) .clusterid.fctr2< 0.5 2136 1010 D (0.4728464 0.5271536)
## 24) Hhold.fctrSKy< 0.5 2015 969 D (0.4808933 0.5191067)
## 48) .clusterid.fctr4< 0.5 1708 846 D (0.4953162 0.5046838)
## 96) .clusterid.fctr3< 0.5 1252 604 R (0.5175719 0.4824281) *
## 97) .clusterid.fctr3>=0.5 456 198 D (0.4342105 0.5657895) *
## 49) .clusterid.fctr4>=0.5 307 123 D (0.4006515 0.5993485) *
## 25) Hhold.fctrSKy>=0.5 121 41 D (0.3388430 0.6611570) *
## 13) .clusterid.fctr2>=0.5 866 344 D (0.3972286 0.6027714)
## 26) Hhold.fctrSKn< 0.5 297 146 R (0.5084175 0.4915825)
## 52) Hhold.fctrMKn< 0.5 172 81 R (0.5290698 0.4709302) *
## 53) Hhold.fctrMKn>=0.5 125 60 D (0.4800000 0.5200000) *
## 27) Hhold.fctrSKn>=0.5 569 193 D (0.3391916 0.6608084) *
## 7) Hhold.fctrPKn>=0.5 150 35 D (0.2333333 0.7666667) *
## [1] "myfit_mdl: train diagnostics complete: 3.365000 secs"
## Prediction
## Reference R D
## R 2056 35
## D 2242 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.48808453 0.03025076 0.47329434 0.50289038 0.52990108
## AccuracyPValue McnemarPValue
## 0.99999999 0.00000000
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 7.861000 secs"
## id feats max.nTuningRuns
## 1 Max.cor.Y##rcv#rpart .clusterid.fctr,Hhold.fctr 5
## min.elapsedtime.everything min.elapsedtime.final max.AUCpROC.fit
## 1 1.769 0.032 0.5841957
## max.Sens.fit max.Spec.fit max.AUCROCR.fit opt.prob.threshold.fit
## 1 0.6537542 0.5146373 0.3939105 0.7
## max.f.score.fit max.Accuracy.fit max.AccuracyLower.fit
## 1 0.6436062 0.5696192 0.4732943
## max.AccuracyUpper.fit max.Kappa.fit max.AUCpROC.OOB max.Sens.OOB
## 1 0.5028904 0.1403183 0.5502714 0.5988593
## max.Spec.OOB max.AUCROCR.OOB opt.prob.threshold.OOB max.f.score.OOB
## 1 0.5016835 0.4470465 0.8 0.6391252
## max.Accuracy.OOB max.AccuracyLower.OOB max.AccuracyUpper.OOB
## 1 0.4696429 0.4400805 0.4993651
## max.Kappa.OOB max.AccuracySD.fit max.KappaSD.fit
## 1 0 0.008483058 0.01667115
## [1] "myfit_mdl: exit: 7.877000 secs"
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Poly"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.day\\.minutes\\.poly\\.", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Poly",
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if ((length(glbFeatsDateTime) > 0) &&
(sum(grepl(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll))) > 0)) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Max.cor.Y.Time.Lag"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars,
grep(paste(names(glbFeatsDateTime), "\\.last[[:digit:]]", sep = ""),
names(glbObsAll), value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Time.Lag",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
if (length(glbFeatsText) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Txt.*"), major.inc = FALSE,
label.minor = "glmnet")
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.(?!([T|P]\\.))", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.nonTP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.T\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyT",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
indepVars <- c(max_cor_y_x_vars)
for (txtFeat in names(glbFeatsText))
indepVars <- union(indepVars,
grep(paste(str_to_upper(substr(txtFeat, 1, 1)), "\\.P\\.", sep = ""),
names(glbObsAll), perl = TRUE, value = TRUE))
indepVars <- myadjustInteractionFeats(glb_feats_df, indepVars)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Max.cor.Y.Text.onlyP",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
# Interactions.High.cor.Y
if (length(int_feats <- setdiff(setdiff(unique(glb_feats_df$cor.high.X), NA),
subset(glb_feats_df, nzv)$id)) > 0) {
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Interact.High.cor.Y"), major.inc = FALSE,
label.minor = "glmnet")
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix="Interact.High.cor.Y",
type=glb_model_type, trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds, trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method="glmnet")),
indepVar=c(max_cor_y_x_vars, paste(max_cor_y_x_vars[1], int_feats, sep=":")),
rsp_var=glb_rsp_var,
fit_df=glbObsFit, OOB_df=glbObsOOB)
}
# Low.cor.X
fit.models_0_chunk_df <- myadd_chunk(fit.models_0_chunk_df,
paste0("fit.models_0_", "Low.cor.X"), major.inc = FALSE,
label.minor = "glmnet")
## label step_major step_minor label_minor bgn
## 4 fit.models_0_Max.cor.Y.rcv.*X* 1 3 glmnet 259.053
## 5 fit.models_0_Low.cor.X 1 4 glmnet 273.095
## end elapsed
## 4 273.094 14.042
## 5 NA NA
indepVar <- mygetIndepVar(glb_feats_df)
ret_lst <- myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = "Low.cor.X",
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds, trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = "glmnet")),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Low.cor.X##rcv#glmnet"
## [1] " indepVar: Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.734000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0216 on full training set
## [1] "myfit_mdl: train complete: 3.855000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = "Low.cor.X", : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 49 -none- numeric
## beta 1372 dgCMatrix S4
## df 49 -none- numeric
## dim 2 -none- numeric
## lambda 49 -none- numeric
## dev.ratio 49 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 28 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.09290574 -0.20257296
## Hhold.fctrPKn Hhold.fctrPKn:.clusterid.fctr2
## 0.54225822 0.18596185
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrMKn:.clusterid.fctr3
## 0.36325477 0.18138347
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr4
## 0.36291452 0.26643652
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.087119921 -0.214397161
## Hhold.fctrPKn Hhold.fctrSKy
## 0.572535797 0.033362924
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## 0.231879816 0.386886071
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3
## 0.236447578 0.409670223
## Hhold.fctrPKy:.clusterid.fctr4 Hhold.fctrSKn:.clusterid.fctr4
## 0.393799216 0.006125892
## [1] "myfit_mdl: train diagnostics complete: 4.524000 secs"
## Prediction
## Reference R D
## R 2056 35
## D 2242 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.48808453 0.03025076 0.47329434 0.50289038 0.52990108
## AccuracyPValue McnemarPValue
## 0.99999999 0.00000000
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 9.026000 secs"
## id feats
## 1 Low.cor.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 3.109 0.104
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5527978 0.3003348 0.8052609 0.3971144
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.65 0.6436062 0.5678975
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4732943 0.5028904 0.1085324
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5229705 0.2395437 0.8063973 0.4464208
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.75 0.6391252 0.4696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4400805 0.4993651 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01478065 0.03075101
## [1] "myfit_mdl: exit: 9.041000 secs"
fit.models_0_chunk_df <-
myadd_chunk(fit.models_0_chunk_df, "fit.models_0_end", major.inc = FALSE,
label.minor = "teardown")
## label step_major step_minor label_minor bgn end
## 5 fit.models_0_Low.cor.X 1 4 glmnet 273.095 282.159
## 6 fit.models_0_end 1 5 teardown 282.160 NA
## elapsed
## 5 9.064
## 6 NA
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
## label step_major step_minor label_minor bgn end elapsed
## 4 fit.models 4 0 0 245.736 282.173 36.437
## 5 fit.models 4 1 1 282.174 NA NA
fit.models_1_chunk_df <- myadd_chunk(NULL, "fit.models_1_bgn", label.minor = "setup")
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_1_bgn 1 0 setup 285.706 NA NA
# refactor code for outliers / ensure all model runs exclude outliers in this chunk ???
#stop(here"); glb2Sav(); all.equal(glb_models_df, sav_models_df)
topindep_var <- NULL; interact_vars <- NULL;
for (mdl_id_pfx in names(glbMdlFamilies)) {
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, paste0("fit.models_1_", mdl_id_pfx),
major.inc = FALSE, label.minor = "setup")
indepVar <- NULL;
if (grepl("\\.Interact", mdl_id_pfx)) {
if (is.null(topindep_var) && is.null(interact_vars)) {
# select best glmnet model upto now
dsp_models_df <- orderBy(model_sel_frmla <- get_model_sel_frmla(),
glb_models_df)
dsp_models_df <- subset(dsp_models_df,
grepl(".glmnet", id, fixed = TRUE))
bst_mdl_id <- dsp_models_df$id[1]
mdl_id_pfx <-
paste(c(head(unlist(strsplit(bst_mdl_id, "[.]")), -1), "Interact"),
collapse=".")
# select important features
if (is.null(bst_featsimp_df <-
myget_feats_importance(glb_models_lst[[bst_mdl_id]]))) {
warning("Base model for RFE.Interact: ", bst_mdl_id,
" has no important features")
next
}
topindep_ix <- 1
while (is.null(topindep_var) && (topindep_ix <= nrow(bst_featsimp_df))) {
topindep_var <- row.names(bst_featsimp_df)[topindep_ix]
if (grepl(".fctr", topindep_var, fixed=TRUE))
topindep_var <-
paste0(unlist(strsplit(topindep_var, ".fctr"))[1], ".fctr")
if (topindep_var %in% names(glbFeatsInteractionOnly)) {
topindep_var <- NULL; topindep_ix <- topindep_ix + 1
} else break
}
# select features with importance > max(10, importance of .rnorm) & is not highest
# combine factor dummy features to just the factor feature
if (length(pos_rnorm <-
grep(".rnorm", row.names(bst_featsimp_df), fixed=TRUE)) > 0)
imp_rnorm <- bst_featsimp_df[pos_rnorm, 1] else
imp_rnorm <- NA
imp_cutoff <- max(10, imp_rnorm, na.rm=TRUE)
interact_vars <-
tail(row.names(subset(bst_featsimp_df,
imp > imp_cutoff)), -1)
if (length(interact_vars) > 0) {
interact_vars <-
myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(interact_vars))
interact_vars <-
interact_vars[!grepl(topindep_var, interact_vars, fixed=TRUE)]
}
### bid0_sp only
# interact_vars <- c(
# "biddable", "D.ratio.sum.TfIdf.wrds.n", "D.TfIdf.sum.stem.stop.Ratio", "D.sum.TfIdf",
# "D.TfIdf.sum.post.stop", "D.TfIdf.sum.post.stem", "D.ratio.wrds.stop.n.wrds.n", "D.chrs.uppr.n.log",
# "D.chrs.n.log", "color.fctr"
# # , "condition.fctr", "prdl.my.descr.fctr"
# )
# interact_vars <- setdiff(interact_vars, c("startprice.dgt2.is9", "color.fctr"))
###
indepVar <- myextract_actual_feats(row.names(bst_featsimp_df))
indepVar <- setdiff(indepVar, topindep_var)
if (length(interact_vars) > 0) {
indepVar <-
setdiff(indepVar, myextract_actual_feats(interact_vars))
indepVar <- c(indepVar,
paste(topindep_var, setdiff(interact_vars, topindep_var),
sep = "*"))
} else indepVar <- union(indepVar, topindep_var)
}
}
if (is.null(indepVar))
indepVar <- glb_mdl_feats_lst[[mdl_id_pfx]]
if (is.null(indepVar) && grepl("RFE\\.", mdl_id_pfx))
indepVar <- myextract_actual_feats(predictors(rfe_fit_results))
if (is.null(indepVar))
indepVar <- mygetIndepVar(glb_feats_df)
if ((length(indepVar) == 1) && (grepl("^%<d-%", indepVar))) {
indepVar <-
eval(parse(text = str_trim(unlist(strsplit(indepVar, "%<d-%"))[2])))
}
indepVar <- myadjustInteractionFeats(glb_feats_df, indepVar)
if (grepl("\\.Interact", mdl_id_pfx)) {
# if (method != tail(unlist(strsplit(bst_mdl_id, "[.]")), 1)) next
if (is.null(glbMdlFamilies[[mdl_id_pfx]])) {
if (!is.null(glbMdlFamilies[["Best.Interact"]]))
glbMdlFamilies[[mdl_id_pfx]] <-
glbMdlFamilies[["Best.Interact"]]
}
}
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
if (is.null(glbMdlFamilies[[mdl_id_pfx]]))
mdl_methods <- glbMdlMethods else
mdl_methods <- glbMdlFamilies[[mdl_id_pfx]]
for (method in mdl_methods) {
if (method %in% c("rpart", "rf")) {
# rpart: fubar's the tree
# rf: skip the scenario w/ .rnorm for speed
indepVar <- setdiff(indepVar, c(".rnorm"))
#mdl_id <- paste0(mdl_id_pfx, ".no.rnorm")
}
fit.models_1_chunk_df <- myadd_chunk(fit.models_1_chunk_df,
paste0("fit.models_1_", mdl_id_pfx), major.inc = FALSE,
label.minor = method)
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type,
tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv", # or "none" if nominalWorkflow is crashing
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = fitobs_df, OOB_df = glbObsOOB)
# ntv_mdl <- glmnet(x = as.matrix(
# fitobs_df[, indepVar]),
# y = as.factor(as.character(
# fitobs_df[, glb_rsp_var])),
# family = "multinomial")
# bgn = 1; end = 100;
# ntv_mdl <- glmnet(x = as.matrix(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, indepVar]),
# y = as.factor(as.character(
# subset(fitobs_df, pop.fctr != "crypto")[bgn:end, glb_rsp_var])),
# family = "multinomial")
}
}
## label step_major step_minor label_minor bgn end
## 1 fit.models_1_bgn 1 0 setup 285.706 285.717
## 2 fit.models_1_All.X 1 1 setup 285.718 NA
## elapsed
## 1 0.011
## 2 NA
## label step_major step_minor label_minor bgn end
## 2 fit.models_1_All.X 1 1 setup 285.718 285.727
## 3 fit.models_1_All.X 1 2 glmnet 285.728 NA
## elapsed
## 2 0.009
## 3 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glmnet"
## [1] " indepVar: Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.736000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.775, lambda = 0.0216 on full training set
## [1] "myfit_mdl: train complete: 3.931000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: lambda
## Length Class Mode
## a0 49 -none- numeric
## beta 1372 dgCMatrix S4
## df 49 -none- numeric
## dim 2 -none- numeric
## lambda 49 -none- numeric
## dev.ratio 49 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 28 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.09290574 -0.20257296
## Hhold.fctrPKn Hhold.fctrPKn:.clusterid.fctr2
## 0.54225822 0.18596185
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrMKn:.clusterid.fctr3
## 0.36325477 0.18138347
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr4
## 0.36291452 0.26643652
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.087119921 -0.214397161
## Hhold.fctrPKn Hhold.fctrSKy
## 0.572535797 0.033362924
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## 0.231879816 0.386886071
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3
## 0.236447578 0.409670223
## Hhold.fctrPKy:.clusterid.fctr4 Hhold.fctrSKn:.clusterid.fctr4
## 0.393799216 0.006125892
## [1] "myfit_mdl: train diagnostics complete: 8.623000 secs"
## Prediction
## Reference R D
## R 2056 35
## D 2242 115
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.48808453 0.03025076 0.47329434 0.50289038 0.52990108
## AccuracyPValue McnemarPValue
## 0.99999999 0.00000000
## Prediction
## Reference R D
## R 526 0
## D 594 0
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.696429e-01 0.000000e+00 4.400805e-01 4.993651e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999790e-01 9.194240e-131
## [1] "myfit_mdl: predict complete: 13.928000 secs"
## id feats
## 1 All.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 3.182 0.102
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5527978 0.3003348 0.8052609 0.3971144
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.65 0.6436062 0.5678975
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4732943 0.5028904 0.1085324
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5229705 0.2395437 0.8063973 0.4464208
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.75 0.6391252 0.4696429
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4400805 0.4993651 0
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01478065 0.03075101
## [1] "myfit_mdl: exit: 13.942000 secs"
## label step_major step_minor label_minor bgn end
## 3 fit.models_1_All.X 1 2 glmnet 285.728 299.676
## 4 fit.models_1_All.X 1 3 glm 299.676 NA
## elapsed
## 3 13.948
## 4 NA
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: All.X##rcv#glm"
## [1] " indepVar: Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.704000 secs"
## + Fold1.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep1: parameter=none
## + Fold2.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep1: parameter=none
## + Fold3.Rep1: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep1: parameter=none
## + Fold1.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep2: parameter=none
## + Fold2.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep2: parameter=none
## + Fold3.Rep2: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep2: parameter=none
## + Fold1.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold1.Rep3: parameter=none
## + Fold2.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold2.Rep3: parameter=none
## + Fold3.Rep3: parameter=none
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## - Fold3.Rep3: parameter=none
## Aggregating results
## Fitting final model on full training set
## [1] "myfit_mdl: train complete: 2.706000 secs"
##
## Call:
## NULL
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.2641 -1.1477 0.8317 1.1598 1.3770
##
## Coefficients: (5 not defined because of singularities)
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) 0.04031 0.15868 0.254 0.799455
## .rnorm -0.02397 0.03036 -0.789 0.429821
## Hhold.fctrMKn -0.08949 0.19452 -0.460 0.645471
## Hhold.fctrMKy -0.42554 0.17319 -2.457 0.014007 *
## Hhold.fctrPKn 0.95893 0.30057 3.190 0.001421 **
## Hhold.fctrPKy -0.03239 0.55763 -0.058 0.953686
## Hhold.fctrSKn -0.14233 0.17446 -0.816 0.414603
## Hhold.fctrSKy 0.76802 0.27394 2.804 0.005054 **
## `Hhold.fctrN:.clusterid.fctr2` -0.10249 0.23565 -0.435 0.663613
## `Hhold.fctrMKn:.clusterid.fctr2` 0.12693 0.21153 0.600 0.548467
## `Hhold.fctrMKy:.clusterid.fctr2` 0.34126 0.15086 2.262 0.023689 *
## `Hhold.fctrPKn:.clusterid.fctr2` 0.83726 0.48039 1.743 0.081355 .
## `Hhold.fctrPKy:.clusterid.fctr2` -0.29242 0.75991 -0.385 0.700382
## `Hhold.fctrSKn:.clusterid.fctr2` 0.76859 0.11445 6.715 1.88e-11 ***
## `Hhold.fctrSKy:.clusterid.fctr2` -1.12282 0.45555 -2.465 0.013711 *
## `Hhold.fctrN:.clusterid.fctr3` 0.11677 0.27957 0.418 0.676174
## `Hhold.fctrMKn:.clusterid.fctr3` 0.92990 0.27749 3.351 0.000805 ***
## `Hhold.fctrMKy:.clusterid.fctr3` 0.98469 0.16040 6.139 8.30e-10 ***
## `Hhold.fctrPKn:.clusterid.fctr3` -0.30930 0.52867 -0.585 0.558521
## `Hhold.fctrPKy:.clusterid.fctr3` -0.19917 0.80801 -0.246 0.805302
## `Hhold.fctrSKn:.clusterid.fctr3` 0.26687 0.13769 1.938 0.052594 .
## `Hhold.fctrSKy:.clusterid.fctr3` -0.58387 0.44708 -1.306 0.191572
## `Hhold.fctrN:.clusterid.fctr4` NA NA NA NA
## `Hhold.fctrMKn:.clusterid.fctr4` NA NA NA NA
## `Hhold.fctrMKy:.clusterid.fctr4` NA NA NA NA
## `Hhold.fctrPKn:.clusterid.fctr4` NA NA NA NA
## `Hhold.fctrPKy:.clusterid.fctr4` 2.49259 1.16944 2.131 0.033052 *
## `Hhold.fctrSKn:.clusterid.fctr4` 0.44626 0.13882 3.215 0.001306 **
## `Hhold.fctrSKy:.clusterid.fctr4` NA NA NA NA
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 6150.3 on 4447 degrees of freedom
## Residual deviance: 5953.4 on 4424 degrees of freedom
## AIC: 6001.4
##
## Number of Fisher Scoring iterations: 4
##
## [1] "myfit_mdl: train diagnostics complete: 3.924000 secs"
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference R D
## R 2040 51
## D 2196 161
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.4948291 0.0415139 0.4800311 0.5096339 0.5299011
## AccuracyPValue McnemarPValue
## 0.9999987 0.0000000
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Warning in predict.lm(object, newdata, se.fit, scale = 1, type =
## ifelse(type == : prediction from a rank-deficient fit may be misleading
## Prediction
## Reference R D
## R 526 0
## D 593 1
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 4.705357e-01 1.581451e-03 4.409678e-01 5.002587e-01 5.303571e-01
## AccuracyPValue McnemarPValue
## 9.999729e-01 1.517149e-130
## [1] "myfit_mdl: predict complete: 11.623000 secs"
## id feats
## 1 All.X##rcv#glm Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 1 1.99 0.075
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5833366 0.6159732 0.5507 0.3857553
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6448554 0.5647478
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4800311 0.5096339 0.1286029
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## 1 0.5576807 0.5665399 0.5488215 0.4324359
## opt.prob.threshold.OOB max.f.score.OOB max.Accuracy.OOB
## 1 0.9 0.6395137 0.4705357
## max.AccuracyLower.OOB max.AccuracyUpper.OOB max.Kappa.OOB
## 1 0.4409678 0.5002587 0.001581451
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.01005124 0.01986131
## [1] "myfit_mdl: exit: 11.638000 secs"
# Check if other preProcess methods improve model performance
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_preProc", major.inc = FALSE,
label.minor = "preProc")
## label step_major step_minor label_minor bgn end
## 4 fit.models_1_All.X 1 3 glm 299.676 311.36
## 5 fit.models_1_preProc 1 4 preProc 311.361 NA
## elapsed
## 4 11.684
## 5 NA
require(gdata)
## Loading required package: gdata
## gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.
##
## gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.
##
## Attaching package: 'gdata'
## The following objects are masked from 'package:dplyr':
##
## combine, first, last
## The following object is masked from 'package:stats':
##
## nobs
## The following object is masked from 'package:utils':
##
## object.size
mdl_id <- orderBy(get_model_sel_frmla(), glb_models_df)[1, "id"]
indepVar <- trim(unlist(strsplit(glb_models_df[glb_models_df$id == mdl_id,
"feats"], "[,]")))
method <- tail(unlist(strsplit(mdl_id, "[.]")), 1)
mdl_id_pfx <- paste0(head(unlist(strsplit(mdl_id, "[.]")), -1), collapse = ".")
if (!is.null(glbObsFitOutliers[[mdl_id_pfx]])) {
fitobs_df <- glbObsFit[!(glbObsFit[, glbFeatsId] %in%
glbObsFitOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsFit) - nrow(fitobs_df)))
print(setdiff(glbObsFit[, glbFeatsId], fitobs_df[, glbFeatsId]))
} else fitobs_df <- glbObsFit
for (prePr in glb_preproc_methods) {
# The operations are applied in this order:
# Box-Cox/Yeo-Johnson transformation, centering, scaling, range, imputation, PCA, ICA then spatial sign.
ret_lst <- myfit_mdl(mdl_specs_lst=myinit_mdl_specs_lst(mdl_specs_lst=list(
id.prefix=mdl_id_pfx,
type=glb_model_type, tune.df=glbMdlTuneParams,
trainControl.method="repeatedcv",
trainControl.number=glb_rcv_n_folds,
trainControl.repeats=glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method=method, train.preProcess=prePr)),
indepVar=indepVar, rsp_var=glb_rsp_var,
fit_df=fitobs_df, OOB_df=glbObsOOB)
}
# If (All|RFE).X.glm is less accurate than Low.Cor.X.glm
# check NA coefficients & filter appropriate terms in indepVar
# if (method == "glm") {
# orig_glm <- glb_models_lst[[paste0(mdl_id, ".", model_method)]]$finalModel
# orig_glm <- glb_models_lst[["All.X.glm"]]$finalModel; print(summary(orig_glm))
# orig_glm <- glb_models_lst[["RFE.X.glm"]]$finalModel; print(summary(orig_glm))
# require(car)
# vif_orig_glm <- vif(orig_glm); print(vif_orig_glm)
# # if vif errors out with "there are aliased coefficients in the model"
# alias_orig_glm <- alias(orig_glm); alias_complete_orig_glm <- (alias_orig_glm$Complete > 0); alias_complete_orig_glm <- alias_complete_orig_glm[rowSums(alias_complete_orig_glm) > 0, colSums(alias_complete_orig_glm) > 0]; print(alias_complete_orig_glm)
# print(vif_orig_glm[!is.na(vif_orig_glm) & (vif_orig_glm == Inf)])
# print(which.max(vif_orig_glm))
# print(sort(vif_orig_glm[vif_orig_glm >= 1.0e+03], decreasing=TRUE))
# glbObsFit[c(1143, 3637, 3953, 4105), c("UniqueID", "Popular", "H.P.quandary", "Headline")]
# glb_feats_df[glb_feats_df$id %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE) | glb_feats_df$cor.high.X %in% grep("[HSA]\\.chrs.n.log", glb_feats_df$id, value=TRUE), ]
# all.equal(glbObsAll$S.chrs.uppr.n.log, glbObsAll$A.chrs.uppr.n.log)
# cor(glbObsAll$S.T.herald, glbObsAll$S.T.tribun)
# mydspObs(Abstract.contains="[Dd]iar", cols=("Abstract"), all=TRUE)
# subset(glb_feats_df, cor.y.abs <= glb_feats_df[glb_feats_df$id == ".rnorm", "cor.y.abs"])
# corxx_mtrx <- cor(data.matrix(glbObsAll[, setdiff(names(glbObsAll), myfind_chr_cols_df(glbObsAll))]), use="pairwise.complete.obs"); abs_corxx_mtrx <- abs(corxx_mtrx); diag(abs_corxx_mtrx) <- 0
# which.max(abs_corxx_mtrx["S.T.tribun", ])
# abs_corxx_mtrx["A.npnct08.log", "S.npnct08.log"]
# step_glm <- step(orig_glm)
# }
# Since caret does not optimize rpart well
# if (method == "rpart")
# ret_lst <- myfit_mdl(mdl_id=paste0(mdl_id_pfx, ".cp.0"), model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=0, tune_models_df=data.frame(parameter="cp", min=0.0, max=0.0, by=0.1))
# User specified
# Ensure at least 2 vars in each regression; else varImp crashes
# sav_models_lst <- glb_models_lst; sav_models_df <- glb_models_df; sav_featsimp_df <- glb_featsimp_df; all.equal(sav_featsimp_df, glb_featsimp_df)
# glb_models_lst <- sav_models_lst; glb_models_df <- sav_models_df; glm_featsimp_df <- sav_featsimp_df
# easier to exclude features
# require(gdata) # needed for trim
# mdl_id <- "";
# indepVar <- head(subset(glb_models_df, grepl("All\\.X\\.", mdl_id), select=feats)
# , 1)[, "feats"]
# indepVar <- trim(unlist(strsplit(indepVar, "[,]")))
# indepVar <- setdiff(indepVar, ".rnorm")
# easier to include features
#stop(here"); sav_models_df <- glb_models_df; glb_models_df <- sav_models_df
# !_sp
# mdl_id <- "csm"; indepVar <- c(NULL
# ,"prdline.my.fctr", "prdline.my.fctr:.clusterid.fctr"
# ,"prdline.my.fctr*biddable"
# #,"prdline.my.fctr*startprice.log"
# #,"prdline.my.fctr*startprice.diff"
# ,"prdline.my.fctr*condition.fctr"
# ,"prdline.my.fctr*D.terms.post.stop.n"
# #,"prdline.my.fctr*D.terms.post.stem.n"
# ,"prdline.my.fctr*cellular.fctr"
# # ,"<feat1>:<feat2>"
# )
# for (method in glbMdlMethods) {
# ret_lst <- myfit_mdl(mdl_id=mdl_id, model_method=method,
# indepVar=indepVar,
# model_type=glb_model_type,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams)
# csm_mdl_id <- paste0(mdl_id, ".", method)
# csm_featsimp_df <- myget_feats_importance(glb_models_lst[[paste0(mdl_id, ".",
# method)]]); print(head(csm_featsimp_df))
# }
###
# Ntv.1.lm <- lm(reformulate(indepVar, glb_rsp_var), glbObsTrn); print(summary(Ntv.1.lm))
#glb_models_df[, "max.Accuracy.OOB", FALSE]
#varImp(glb_models_lst[["Low.cor.X.glm"]])
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.2.glm"]])$imp)
#orderBy(~ -Overall, varImp(glb_models_lst[["All.X.3.glm"]])$imp)
#glb_feats_df[grepl("npnct28", glb_feats_df$id), ]
# User specified bivariate models
# indepVar_lst <- list()
# for (feat in setdiff(names(glbObsFit),
# union(glb_rsp_var, glbFeatsExclude)))
# indepVar_lst[["feat"]] <- feat
# User specified combinatorial models
# indepVar_lst <- list()
# combn_mtrx <- combn(c("<feat1_name>", "<feat2_name>", "<featn_name>"),
# <num_feats_to_choose>)
# for (combn_ix in 1:ncol(combn_mtrx))
# #print(combn_mtrx[, combn_ix])
# indepVar_lst[[combn_ix]] <- combn_mtrx[, combn_ix]
# template for myfit_mdl
# rf is hard-coded in caret to recognize only Accuracy / Kappa evaluation metrics
# only for OOB in trainControl ?
# ret_lst <- myfit_mdl_fn(mdl_id=paste0(mdl_id_pfx, ""), model_method=method,
# indepVar=indepVar,
# rsp_var=glb_rsp_var,
# fit_df=glbObsFit, OOB_df=glbObsOOB,
# n_cv_folds=glb_rcv_n_folds, tune_models_df=glbMdlTuneParams,
# model_loss_mtrx=glbMdlMetric_terms,
# model_summaryFunction=glbMdlMetricSummaryFn,
# model_metric=glbMdlMetricSummary,
# model_metric_maximize=glbMdlMetricMaximize)
# Simplify a model
# fit_df <- glbObsFit; glb_mdl <- step(<complex>_mdl)
# Non-caret models
# rpart_area_mdl <- rpart(reformulate("Area", response=glb_rsp_var),
# data=glbObsFit, #method="class",
# control=rpart.control(cp=0.12),
# parms=list(loss=glbMdlMetric_terms))
# print("rpart_sel_wlm_mdl"); prp(rpart_sel_wlm_mdl)
#
print(glb_models_df)
## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet .clusterid.fctr,Hhold.fctr
## Max.cor.Y##rcv#rpart .clusterid.fctr,Hhold.fctr
## Low.cor.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything
## MFO###myMFO_classfr 0 0.491
## Random###myrandom_classfr 0 0.291
## Max.cor.Y.rcv.1X1###glmnet 0 0.823
## Max.cor.Y##rcv#rpart 5 1.769
## Low.cor.X##rcv#glmnet 20 3.109
## All.X##rcv#glmnet 20 3.182
## All.X##rcv#glm 1 1.990
## min.elapsedtime.final max.AUCpROC.fit
## MFO###myMFO_classfr 0.004 0.5000000
## Random###myrandom_classfr 0.003 0.4942483
## Max.cor.Y.rcv.1X1###glmnet 0.074 0.5754816
## Max.cor.Y##rcv#rpart 0.032 0.5841957
## Low.cor.X##rcv#glmnet 0.104 0.5527978
## All.X##rcv#glmnet 0.102 0.5527978
## All.X##rcv#glm 0.075 0.5833366
## max.Sens.fit max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.4619799 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5557150 0.5952482 0.4065966
## Max.cor.Y##rcv#rpart 0.6537542 0.5146373 0.3939105
## Low.cor.X##rcv#glmnet 0.3003348 0.8052609 0.3971144
## All.X##rcv#glmnet 0.3003348 0.8052609 0.3971144
## All.X##rcv#glm 0.6159732 0.5507000 0.3857553
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.6395473
## Random###myrandom_classfr 0.55 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.70 0.6436062
## Max.cor.Y##rcv#rpart 0.70 0.6436062
## Low.cor.X##rcv#glmnet 0.65 0.6436062
## All.X##rcv#glmnet 0.65 0.6436062
## All.X##rcv#glm 0.70 0.6448554
## max.Accuracy.fit max.AccuracyLower.fit
## MFO###myMFO_classfr 0.4700989 0.4553427
## Random###myrandom_classfr 0.4700989 0.4553427
## Max.cor.Y.rcv.1X1###glmnet 0.4880845 0.4732943
## Max.cor.Y##rcv#rpart 0.5696192 0.4732943
## Low.cor.X##rcv#glmnet 0.5678975 0.4732943
## All.X##rcv#glmnet 0.5678975 0.4732943
## All.X##rcv#glm 0.5647478 0.4800311
## max.AccuracyUpper.fit max.Kappa.fit
## MFO###myMFO_classfr 0.4848945 0.00000000
## Random###myrandom_classfr 0.4848945 0.00000000
## Max.cor.Y.rcv.1X1###glmnet 0.5028904 0.03025076
## Max.cor.Y##rcv#rpart 0.5028904 0.14031827
## Low.cor.X##rcv#glmnet 0.5028904 0.10853242
## All.X##rcv#glmnet 0.5028904 0.10853242
## All.X##rcv#glm 0.5096339 0.12860295
## max.AUCpROC.OOB max.Sens.OOB max.Spec.OOB
## MFO###myMFO_classfr 0.5000000 0.0000000 1.0000000
## Random###myrandom_classfr 0.5235690 0.5000000 0.5471380
## Max.cor.Y.rcv.1X1###glmnet 0.5546626 0.5285171 0.5808081
## Max.cor.Y##rcv#rpart 0.5502714 0.5988593 0.5016835
## Low.cor.X##rcv#glmnet 0.5229705 0.2395437 0.8063973
## All.X##rcv#glmnet 0.5229705 0.2395437 0.8063973
## All.X##rcv#glm 0.5576807 0.5665399 0.5488215
## max.AUCROCR.OOB opt.prob.threshold.OOB
## MFO###myMFO_classfr 0.5000000 0.50
## Random###myrandom_classfr 0.5191202 0.55
## Max.cor.Y.rcv.1X1###glmnet 0.4525163 0.85
## Max.cor.Y##rcv#rpart 0.4470465 0.80
## Low.cor.X##rcv#glmnet 0.4464208 0.75
## All.X##rcv#glmnet 0.4464208 0.75
## All.X##rcv#glm 0.4324359 0.90
## max.f.score.OOB max.Accuracy.OOB
## MFO###myMFO_classfr 0.6391252 0.4696429
## Random###myrandom_classfr 0.6391252 0.4696429
## Max.cor.Y.rcv.1X1###glmnet 0.6391252 0.4696429
## Max.cor.Y##rcv#rpart 0.6391252 0.4696429
## Low.cor.X##rcv#glmnet 0.6391252 0.4696429
## All.X##rcv#glmnet 0.6391252 0.4696429
## All.X##rcv#glm 0.6395137 0.4705357
## max.AccuracyLower.OOB max.AccuracyUpper.OOB
## MFO###myMFO_classfr 0.4400805 0.4993651
## Random###myrandom_classfr 0.4400805 0.4993651
## Max.cor.Y.rcv.1X1###glmnet 0.4400805 0.4993651
## Max.cor.Y##rcv#rpart 0.4400805 0.4993651
## Low.cor.X##rcv#glmnet 0.4400805 0.4993651
## All.X##rcv#glmnet 0.4400805 0.4993651
## All.X##rcv#glm 0.4409678 0.5002587
## max.Kappa.OOB max.AccuracySD.fit
## MFO###myMFO_classfr 0.000000000 NA
## Random###myrandom_classfr 0.000000000 NA
## Max.cor.Y.rcv.1X1###glmnet 0.000000000 NA
## Max.cor.Y##rcv#rpart 0.000000000 0.008483058
## Low.cor.X##rcv#glmnet 0.000000000 0.014780651
## All.X##rcv#glmnet 0.000000000 0.014780651
## All.X##rcv#glm 0.001581451 0.010051240
## max.KappaSD.fit
## MFO###myMFO_classfr NA
## Random###myrandom_classfr NA
## Max.cor.Y.rcv.1X1###glmnet NA
## Max.cor.Y##rcv#rpart 0.01667115
## Low.cor.X##rcv#glmnet 0.03075101
## All.X##rcv#glmnet 0.03075101
## All.X##rcv#glm 0.01986131
rm(ret_lst)
fit.models_1_chunk_df <-
myadd_chunk(fit.models_1_chunk_df, "fit.models_1_end", major.inc = FALSE,
label.minor = "teardown")
## label step_major step_minor label_minor bgn end
## 5 fit.models_1_preProc 1 4 preProc 311.361 312.879
## 6 fit.models_1_end 1 5 teardown 312.879 NA
## elapsed
## 5 1.518
## 6 NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc = FALSE)
## label step_major step_minor label_minor bgn end elapsed
## 5 fit.models 4 1 1 282.174 312.889 30.716
## 6 fit.models 4 2 2 312.890 NA NA
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "setup")
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 setup 316.868 NA NA
plt_models_df <- glb_models_df[, -grep("SD|Upper|Lower", names(glb_models_df))]
for (var in grep("^min.", names(plt_models_df), value=TRUE)) {
plt_models_df[, sub("min.", "inv.", var)] <-
#ifelse(all(is.na(tmp <- plt_models_df[, var])), NA, 1.0 / tmp)
1.0 / plt_models_df[, var]
plt_models_df <- plt_models_df[ , -grep(var, names(plt_models_df))]
}
print(plt_models_df)
## id
## MFO###myMFO_classfr MFO###myMFO_classfr
## Random###myrandom_classfr Random###myrandom_classfr
## Max.cor.Y.rcv.1X1###glmnet Max.cor.Y.rcv.1X1###glmnet
## Max.cor.Y##rcv#rpart Max.cor.Y##rcv#rpart
## Low.cor.X##rcv#glmnet Low.cor.X##rcv#glmnet
## All.X##rcv#glmnet All.X##rcv#glmnet
## All.X##rcv#glm All.X##rcv#glm
## feats
## MFO###myMFO_classfr .rnorm
## Random###myrandom_classfr .rnorm
## Max.cor.Y.rcv.1X1###glmnet .clusterid.fctr,Hhold.fctr
## Max.cor.Y##rcv#rpart .clusterid.fctr,Hhold.fctr
## Low.cor.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## All.X##rcv#glm Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns max.AUCpROC.fit max.Sens.fit
## MFO###myMFO_classfr 0 0.5000000 0.0000000
## Random###myrandom_classfr 0 0.4942483 0.4619799
## Max.cor.Y.rcv.1X1###glmnet 0 0.5754816 0.5557150
## Max.cor.Y##rcv#rpart 5 0.5841957 0.6537542
## Low.cor.X##rcv#glmnet 20 0.5527978 0.3003348
## All.X##rcv#glmnet 20 0.5527978 0.3003348
## All.X##rcv#glm 1 0.5833366 0.6159732
## max.Spec.fit max.AUCROCR.fit
## MFO###myMFO_classfr 1.0000000 0.5000000
## Random###myrandom_classfr 0.5265168 0.5073101
## Max.cor.Y.rcv.1X1###glmnet 0.5952482 0.4065966
## Max.cor.Y##rcv#rpart 0.5146373 0.3939105
## Low.cor.X##rcv#glmnet 0.8052609 0.3971144
## All.X##rcv#glmnet 0.8052609 0.3971144
## All.X##rcv#glm 0.5507000 0.3857553
## opt.prob.threshold.fit max.f.score.fit
## MFO###myMFO_classfr 0.50 0.6395473
## Random###myrandom_classfr 0.55 0.6395473
## Max.cor.Y.rcv.1X1###glmnet 0.70 0.6436062
## Max.cor.Y##rcv#rpart 0.70 0.6436062
## Low.cor.X##rcv#glmnet 0.65 0.6436062
## All.X##rcv#glmnet 0.65 0.6436062
## All.X##rcv#glm 0.70 0.6448554
## max.Accuracy.fit max.Kappa.fit max.AUCpROC.OOB
## MFO###myMFO_classfr 0.4700989 0.00000000 0.5000000
## Random###myrandom_classfr 0.4700989 0.00000000 0.5235690
## Max.cor.Y.rcv.1X1###glmnet 0.4880845 0.03025076 0.5546626
## Max.cor.Y##rcv#rpart 0.5696192 0.14031827 0.5502714
## Low.cor.X##rcv#glmnet 0.5678975 0.10853242 0.5229705
## All.X##rcv#glmnet 0.5678975 0.10853242 0.5229705
## All.X##rcv#glm 0.5647478 0.12860295 0.5576807
## max.Sens.OOB max.Spec.OOB max.AUCROCR.OOB
## MFO###myMFO_classfr 0.0000000 1.0000000 0.5000000
## Random###myrandom_classfr 0.5000000 0.5471380 0.5191202
## Max.cor.Y.rcv.1X1###glmnet 0.5285171 0.5808081 0.4525163
## Max.cor.Y##rcv#rpart 0.5988593 0.5016835 0.4470465
## Low.cor.X##rcv#glmnet 0.2395437 0.8063973 0.4464208
## All.X##rcv#glmnet 0.2395437 0.8063973 0.4464208
## All.X##rcv#glm 0.5665399 0.5488215 0.4324359
## opt.prob.threshold.OOB max.f.score.OOB
## MFO###myMFO_classfr 0.50 0.6391252
## Random###myrandom_classfr 0.55 0.6391252
## Max.cor.Y.rcv.1X1###glmnet 0.85 0.6391252
## Max.cor.Y##rcv#rpart 0.80 0.6391252
## Low.cor.X##rcv#glmnet 0.75 0.6391252
## All.X##rcv#glmnet 0.75 0.6391252
## All.X##rcv#glm 0.90 0.6395137
## max.Accuracy.OOB max.Kappa.OOB
## MFO###myMFO_classfr 0.4696429 0.000000000
## Random###myrandom_classfr 0.4696429 0.000000000
## Max.cor.Y.rcv.1X1###glmnet 0.4696429 0.000000000
## Max.cor.Y##rcv#rpart 0.4696429 0.000000000
## Low.cor.X##rcv#glmnet 0.4696429 0.000000000
## All.X##rcv#glmnet 0.4696429 0.000000000
## All.X##rcv#glm 0.4705357 0.001581451
## inv.elapsedtime.everything
## MFO###myMFO_classfr 2.0366599
## Random###myrandom_classfr 3.4364261
## Max.cor.Y.rcv.1X1###glmnet 1.2150668
## Max.cor.Y##rcv#rpart 0.5652911
## Low.cor.X##rcv#glmnet 0.3216468
## All.X##rcv#glmnet 0.3142678
## All.X##rcv#glm 0.5025126
## inv.elapsedtime.final
## MFO###myMFO_classfr 250.000000
## Random###myrandom_classfr 333.333333
## Max.cor.Y.rcv.1X1###glmnet 13.513514
## Max.cor.Y##rcv#rpart 31.250000
## Low.cor.X##rcv#glmnet 9.615385
## All.X##rcv#glmnet 9.803922
## All.X##rcv#glm 13.333333
# print(myplot_radar(radar_inp_df=plt_models_df))
# print(myplot_radar(radar_inp_df=subset(plt_models_df,
# !(mdl_id %in% grep("random|MFO", plt_models_df$id, value=TRUE)))))
# Compute CI for <metric>SD
glb_models_df <- mutate(glb_models_df,
max.df = ifelse(max.nTuningRuns > 1, max.nTuningRuns - 1, NA),
min.sd2ci.scaler = ifelse(is.na(max.df), NA, qt(0.975, max.df)))
for (var in grep("SD", names(glb_models_df), value=TRUE)) {
# Does CI alredy exist ?
var_components <- unlist(strsplit(var, "SD"))
varActul <- paste0(var_components[1], var_components[2])
varUpper <- paste0(var_components[1], "Upper", var_components[2])
varLower <- paste0(var_components[1], "Lower", var_components[2])
if (varUpper %in% names(glb_models_df)) {
warning(varUpper, " already exists in glb_models_df")
# Assuming Lower also exists
next
}
print(sprintf("var:%s", var))
# CI is dependent on sample size in t distribution; df=n-1
glb_models_df[, varUpper] <- glb_models_df[, varActul] +
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
glb_models_df[, varLower] <- glb_models_df[, varActul] -
glb_models_df[, "min.sd2ci.scaler"] * glb_models_df[, var]
}
## Warning: max.AccuracyUpper.fit already exists in glb_models_df
## [1] "var:max.KappaSD.fit"
# Plot metrics with CI
plt_models_df <- glb_models_df[, "id", FALSE]
pltCI_models_df <- glb_models_df[, "id", FALSE]
for (var in grep("Upper", names(glb_models_df), value=TRUE)) {
var_components <- unlist(strsplit(var, "Upper"))
col_name <- unlist(paste(var_components, collapse=""))
plt_models_df[, col_name] <- glb_models_df[, col_name]
for (name in paste0(var_components[1], c("Upper", "Lower"), var_components[2]))
pltCI_models_df[, name] <- glb_models_df[, name]
}
build_statsCI_data <- function(plt_models_df) {
mltd_models_df <- melt(plt_models_df, id.vars="id")
mltd_models_df$data <- sapply(1:nrow(mltd_models_df),
function(row_ix) tail(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]), "[.]")), 1))
mltd_models_df$label <- sapply(1:nrow(mltd_models_df),
function(row_ix) head(unlist(strsplit(as.character(
mltd_models_df[row_ix, "variable"]),
paste0(".", mltd_models_df[row_ix, "data"]))), 1))
#print(mltd_models_df)
return(mltd_models_df)
}
mltd_models_df <- build_statsCI_data(plt_models_df)
mltdCI_models_df <- melt(pltCI_models_df, id.vars="id")
for (row_ix in 1:nrow(mltdCI_models_df)) {
for (type in c("Upper", "Lower")) {
if (length(var_components <- unlist(strsplit(
as.character(mltdCI_models_df[row_ix, "variable"]), type))) > 1) {
#print(sprintf("row_ix:%d; type:%s; ", row_ix, type))
mltdCI_models_df[row_ix, "label"] <- var_components[1]
mltdCI_models_df[row_ix, "data"] <-
unlist(strsplit(var_components[2], "[.]"))[2]
mltdCI_models_df[row_ix, "type"] <- type
break
}
}
}
wideCI_models_df <- reshape(subset(mltdCI_models_df, select=-variable),
timevar="type",
idvar=setdiff(names(mltdCI_models_df), c("type", "value", "variable")),
direction="wide")
#print(wideCI_models_df)
mrgdCI_models_df <- merge(wideCI_models_df, mltd_models_df, all.x=TRUE)
#print(mrgdCI_models_df)
# Merge stats back in if CIs don't exist
goback_vars <- c()
for (var in unique(mltd_models_df$label)) {
for (type in unique(mltd_models_df$data)) {
var_type <- paste0(var, ".", type)
# if this data is already present, next
if (var_type %in% unique(paste(mltd_models_df$label, mltd_models_df$data,
sep=".")))
next
#print(sprintf("var_type:%s", var_type))
goback_vars <- c(goback_vars, var_type)
}
}
if (length(goback_vars) > 0) {
mltd_goback_df <- build_statsCI_data(glb_models_df[, c("id", goback_vars)])
mltd_models_df <- rbind(mltd_models_df, mltd_goback_df)
}
# mltd_models_df <- merge(mltd_models_df, glb_models_df[, c("id", "model_method")],
# all.x=TRUE)
png(paste0(glbOut$pfx, "models_bar.png"), width=480*3, height=480*2)
#print(gp <- myplot_bar(mltd_models_df, "id", "value", colorcol_name="model_method") +
print(gp <- myplot_bar(df=mltd_models_df, xcol_name="id", ycol_names="value") +
geom_errorbar(data=mrgdCI_models_df,
mapping=aes(x=mdl_id, ymax=value.Upper, ymin=value.Lower), width=0.5) +
facet_grid(label ~ data, scales="free") +
theme(axis.text.x = element_text(angle = 90,vjust = 0.5)))
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dev.off()
## quartz_off_screen
## 2
print(gp)
## Warning: Removed 4 rows containing missing values (geom_errorbar).
dsp_models_cols <- c("id",
glbMdlMetricsEval[glbMdlMetricsEval %in% names(glb_models_df)],
grep("opt.", names(glb_models_df), fixed = TRUE, value = TRUE))
# if (glb_is_classification && glb_is_binomial)
# dsp_models_cols <- c(dsp_models_cols, "opt.prob.threshold.OOB")
print(dsp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)[, dsp_models_cols])
## id max.Accuracy.OOB max.AUCROCR.OOB
## 7 All.X##rcv#glm 0.4705357 0.4324359
## 2 Random###myrandom_classfr 0.4696429 0.5191202
## 1 MFO###myMFO_classfr 0.4696429 0.5000000
## 3 Max.cor.Y.rcv.1X1###glmnet 0.4696429 0.4525163
## 4 Max.cor.Y##rcv#rpart 0.4696429 0.4470465
## 5 Low.cor.X##rcv#glmnet 0.4696429 0.4464208
## 6 All.X##rcv#glmnet 0.4696429 0.4464208
## max.AUCpROC.OOB max.Accuracy.fit opt.prob.threshold.fit
## 7 0.5576807 0.5647478 0.70
## 2 0.5235690 0.4700989 0.55
## 1 0.5000000 0.4700989 0.50
## 3 0.5546626 0.4880845 0.70
## 4 0.5502714 0.5696192 0.70
## 5 0.5229705 0.5678975 0.65
## 6 0.5229705 0.5678975 0.65
## opt.prob.threshold.OOB
## 7 0.90
## 2 0.55
## 1 0.50
## 3 0.85
## 4 0.80
## 5 0.75
## 6 0.75
# print(myplot_radar(radar_inp_df = dsp_models_df))
print("Metrics used for model selection:"); print(get_model_sel_frmla())
## [1] "Metrics used for model selection:"
## ~-max.Accuracy.OOB - max.AUCROCR.OOB - max.AUCpROC.OOB - max.Accuracy.fit -
## opt.prob.threshold.OOB
## <environment: 0x7fcdf18ee3b8>
print(sprintf("Best model id: %s", dsp_models_df[1, "id"]))
## [1] "Best model id: All.X##rcv#glm"
glb_get_predictions <- function(df, mdl_id, rsp_var, prob_threshold_def=NULL, verbose=FALSE) {
mdl <- glb_models_lst[[mdl_id]]
clmnNames <- mygetPredictIds(rsp_var, mdl_id)
predct_var_name <- clmnNames$value
predct_prob_var_name <- clmnNames$prob
predct_accurate_var_name <- clmnNames$is.acc
predct_error_var_name <- clmnNames$err
predct_erabs_var_name <- clmnNames$err.abs
if (glb_is_regression) {
df[, predct_var_name] <- predict(mdl, newdata=df, type="raw")
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] - df[, glb_rsp_var]
if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="auto"))
if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
#facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
stat_smooth(method="glm"))
df[, predct_erabs_var_name] <- abs(df[, predct_error_var_name])
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && glb_is_binomial) {
prob_threshold <- glb_models_df[glb_models_df$id == mdl_id,
"opt.prob.threshold.OOB"]
if (is.null(prob_threshold) || is.na(prob_threshold)) {
warning("Using default probability threshold: ", prob_threshold_def)
if (is.null(prob_threshold <- prob_threshold_def))
stop("Default probability threshold is NULL")
}
df[, predct_prob_var_name] <- predict(mdl, newdata = df, type = "prob")[, 2]
df[, predct_var_name] <-
factor(levels(df[, glb_rsp_var])[
(df[, predct_prob_var_name] >=
prob_threshold) * 1 + 1], levels(df[, glb_rsp_var]))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_var_name) +
# facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if (verbose) print(myplot_scatter(df, predct_var_name, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="auto"))
# if (verbose) print(myplot_scatter(df, glb_rsp_var, predct_error_var_name) +
# #facet_wrap(reformulate(glbFeatsCategory), scales = "free") +
# stat_smooth(method="glm"))
# if prediction is a TP (true +ve), measure distance from 1.0
tp <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[tp, predct_erabs_var_name] <- abs(1 - df[tp, predct_prob_var_name])
#rowIx <- which.max(df[tp, predct_erabs_var_name]); df[tp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a TN (true -ve), measure distance from 0.0
tn <- which((df[, predct_var_name] == df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[tn, predct_erabs_var_name] <- abs(0 - df[tn, predct_prob_var_name])
#rowIx <- which.max(df[tn, predct_erabs_var_name]); df[tn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FP (flse +ve), measure distance from 0.0
fp <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[2]))
df[fp, predct_erabs_var_name] <- abs(0 - df[fp, predct_prob_var_name])
#rowIx <- which.max(df[fp, predct_erabs_var_name]); df[fp, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
# if prediction is a FN (flse -ve), measure distance from 1.0
fn <- which((df[, predct_var_name] != df[, glb_rsp_var]) &
(df[, predct_var_name] == levels(df[, glb_rsp_var])[1]))
df[fn, predct_erabs_var_name] <- abs(1 - df[fn, predct_prob_var_name])
#rowIx <- which.max(df[fn, predct_erabs_var_name]); df[fn, c(glbFeatsId, glb_rsp_var, predct_var_name, predct_prob_var_name, predct_erabs_var_name)][rowIx, ]
if (verbose) print(head(orderBy(reformulate(c("-", predct_erabs_var_name)), df)))
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
if (glb_is_classification && !glb_is_binomial) {
df[, predct_var_name] <- predict(mdl, newdata = df, type = "raw")
probCls <- predict(mdl, newdata = df, type = "prob")
df[, predct_prob_var_name] <- NA
for (cls in names(probCls)) {
mask <- (df[, predct_var_name] == cls)
df[mask, predct_prob_var_name] <- probCls[mask, cls]
}
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
fill_col_name = predct_var_name))
if (verbose) print(myplot_histogram(df, predct_prob_var_name,
facet_frmla = paste0("~", glb_rsp_var)))
df[, predct_error_var_name] <- df[, predct_var_name] != df[, glb_rsp_var]
# if prediction is erroneous, measure predicted class prob from actual class prob
df[, predct_erabs_var_name] <- 0
for (cls in names(probCls)) {
mask <- (df[, glb_rsp_var] == cls) & (df[, predct_error_var_name])
df[mask, predct_erabs_var_name] <- probCls[mask, cls]
}
df[, predct_accurate_var_name] <- (df[, glb_rsp_var] == df[, predct_var_name])
}
return(df)
}
#stop(here"); glb2Sav(); glbObsAll <- savObsAll; glbObsTrn <- savObsTrn; glbObsFit <- savObsFit; glbObsOOB <- savObsOOB; sav_models_df <- glb_models_df; glb_models_df <- sav_models_df; glb_featsimp_df <- sav_featsimp_df
myget_category_stats <- function(obs_df, mdl_id, label) {
require(dplyr)
require(lazyeval)
predct_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$value
predct_error_var_name <- mygetPredictIds(glb_rsp_var, mdl_id)$err.abs
if (!predct_var_name %in% names(obs_df))
obs_df <- glb_get_predictions(obs_df, mdl_id, glb_rsp_var)
tmp_obs_df <- obs_df[, c(glbFeatsCategory, glb_rsp_var,
predct_var_name, predct_error_var_name)]
# tmp_obs_df <- obs_df %>%
# dplyr::select_(glbFeatsCategory, glb_rsp_var, predct_var_name, predct_error_var_name)
#dplyr::rename(startprice.log10.predict.RFE.X.glmnet.err=error_abs_OOB)
names(tmp_obs_df)[length(names(tmp_obs_df))] <- paste0("err.abs.", label)
ret_ctgry_df <- tmp_obs_df %>%
dplyr::group_by_(glbFeatsCategory) %>%
dplyr::summarise_(#interp(~sum(abs(var)), var=as.name(glb_rsp_var)),
interp(~sum(var), var=as.name(paste0("err.abs.", label))),
interp(~mean(var), var=as.name(paste0("err.abs.", label))),
interp(~n()))
names(ret_ctgry_df) <- c(glbFeatsCategory,
#paste0(glb_rsp_var, ".abs.", label, ".sum"),
paste0("err.abs.", label, ".sum"),
paste0("err.abs.", label, ".mean"),
paste0(".n.", label))
ret_ctgry_df <- dplyr::ungroup(ret_ctgry_df)
#colSums(ret_ctgry_df[, -grep(glbFeatsCategory, names(ret_ctgry_df))])
return(ret_ctgry_df)
}
#print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
if (!is.null(glb_mdl_ensemble)) {
fit.models_2_chunk_df <- myadd_chunk(fit.models_2_chunk_df,
paste0("fit.models_2_", mdl_id_pfx), major.inc = TRUE,
label.minor = "ensemble")
mdl_id_pfx <- "Ensemble"
if (#(glb_is_regression) |
((glb_is_classification) & (!glb_is_binomial)))
stop("Ensemble models not implemented yet for multinomial classification")
mygetEnsembleAutoMdlIds <- function() {
tmp_models_df <- orderBy(get_model_sel_frmla(), glb_models_df)
row.names(tmp_models_df) <- tmp_models_df$id
mdl_threshold_pos <-
min(which(grepl("MFO|Random|Baseline", tmp_models_df$id))) - 1
mdlIds <- tmp_models_df$id[1:mdl_threshold_pos]
return(mdlIds[!grepl("Ensemble", mdlIds)])
}
if (glb_mdl_ensemble == "auto") {
glb_mdl_ensemble <- mygetEnsembleAutoMdlIds()
mdl_id_pfx <- paste0(mdl_id_pfx, ".auto")
} else if (grepl("^%<d-%", glb_mdl_ensemble)) {
glb_mdl_ensemble <- eval(parse(text =
str_trim(unlist(strsplit(glb_mdl_ensemble, "%<d-%"))[2])))
}
for (mdl_id in glb_mdl_ensemble) {
if (!(mdl_id %in% names(glb_models_lst))) {
warning("Model ", mdl_id, " in glb_model_ensemble not found !")
next
}
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id, glb_rsp_var)
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id, glb_rsp_var)
}
#mdl_id_pfx <- "Ensemble.RFE"; mdlId <- paste0(mdl_id_pfx, ".glmnet")
#glb_mdl_ensemble <- gsub(mygetPredictIds$value, "", grep("RFE\\.X\\.(?!Interact)", row.names(glb_featsimp_df), perl = TRUE, value = TRUE), fixed = TRUE)
#varImp(glb_models_lst[[mdlId]])
#cor_df <- data.frame(cor=cor(glbObsFit[, glb_rsp_var], glbObsFit[, paste(mygetPredictIds$value, glb_mdl_ensemble)], use="pairwise.complete.obs"))
#glbObsFit <- glb_get_predictions(df=glbObsFit, "Ensemble.glmnet", glb_rsp_var);print(colSums((ctgry_df <- myget_category_stats(obs_df=glbObsFit, mdl_id="Ensemble.glmnet", label="fit"))[, -grep(glbFeatsCategory, names(ctgry_df))]))
### bid0_sp
# Better than MFO; models.n=28; min.RMSE.fit=0.0521233; err.abs.fit.sum=7.3631895
# old: Top x from auto; models.n= 5; min.RMSE.fit=0.06311047; err.abs.fit.sum=9.5937080
# RFE only ; models.n=16; min.RMSE.fit=0.05148588; err.abs.fit.sum=7.2875091
# RFE subset only ;models.n= 5; min.RMSE.fit=0.06040702; err.abs.fit.sum=9.059088
# RFE subset only ;models.n= 9; min.RMSE.fit=0.05933167; err.abs.fit.sum=8.7421288
# RFE subset only ;models.n=15; min.RMSE.fit=0.0584607; err.abs.fit.sum=8.5902066
# RFE subset only ;models.n=17; min.RMSE.fit=0.05496899; err.abs.fit.sum=8.0170431
# RFE subset only ;models.n=18; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
# RFE subset only ;models.n=16; min.RMSE.fit=0.05441577; err.abs.fit.sum=7.837223
### bid0_sp
### bid1_sp
# "auto"; err.abs.fit.sum=76.699774; min.RMSE.fit=0.2186429
# "RFE.X.*"; err.abs.fit.sum=; min.RMSE.fit=0.221114
### bid1_sp
indepVar <- paste(mygetPredictIds(glb_rsp_var)$value, glb_mdl_ensemble, sep = "")
if (glb_is_classification)
indepVar <- paste(indepVar, ".prob", sep = "")
# Some models in glb_mdl_ensemble might not be fitted e.g. RFE.X.Interact
indepVar <- intersect(indepVar, names(glbObsFit))
# indepVar <- grep(mygetPredictIds(glb_rsp_var)$value, names(glbObsFit), fixed=TRUE, value=TRUE)
# if (glb_is_regression)
# indepVar <- indepVar[!grepl("(err\\.abs|accurate)$", indepVar)]
# if (glb_is_classification && glb_is_binomial)
# indepVar <- grep("prob$", indepVar, value=TRUE) else
# indepVar <- indepVar[!grepl("err$", indepVar)]
#rfe_fit_ens_results <- myrun_rfe(glbObsFit, indepVar)
for (method in c("glm", "glmnet")) {
for (trainControlMethod in
c("boot", "boot632", "cv", "repeatedcv"
#, "LOOCV" # tuneLength * nrow(fitDF)
, "LGOCV", "adaptive_cv"
#, "adaptive_boot" #error: adaptive$min should be less than 3
#, "adaptive_LGOCV" #error: adaptive$min should be less than 3
)) {
#sav_models_df <- glb_models_df; all.equal(sav_models_df, glb_models_df)
#glb_models_df <- sav_models_df; print(glb_models_df$id)
if ((method == "glm") && (trainControlMethod != "repeatedcv"))
# glm used only to identify outliers
next
ret_lst <- myfit_mdl(
mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = paste0(mdl_id_pfx, ".", trainControlMethod),
type = glb_model_type, tune.df = NULL,
trainControl.method = trainControlMethod,
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = glbObsFit, OOB_df = glbObsOOB)
}
}
dsp_models_df <- get_dsp_models_df()
}
if (is.null(glbMdlSelId))
glbMdlSelId <- dsp_models_df[1, "id"] else
print(sprintf("User specified selection: %s", glbMdlSelId))
## [1] "User specified selection: All.X##rcv#glmnet"
myprint_mdl(glb_sel_mdl <- glb_models_lst[[glbMdlSelId]])
## Length Class Mode
## a0 49 -none- numeric
## beta 1372 dgCMatrix S4
## df 49 -none- numeric
## dim 2 -none- numeric
## lambda 49 -none- numeric
## dev.ratio 49 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 28 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.09290574 -0.20257296
## Hhold.fctrPKn Hhold.fctrPKn:.clusterid.fctr2
## 0.54225822 0.18596185
## Hhold.fctrSKn:.clusterid.fctr2 Hhold.fctrMKn:.clusterid.fctr3
## 0.36325477 0.18138347
## Hhold.fctrMKy:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr4
## 0.36291452 0.26643652
## [1] "max lambda < lambdaOpt:"
## (Intercept) Hhold.fctrMKy
## 0.087119921 -0.214397161
## Hhold.fctrPKn Hhold.fctrSKy
## 0.572535797 0.033362924
## Hhold.fctrPKn:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## 0.231879816 0.386886071
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3
## 0.236447578 0.409670223
## Hhold.fctrPKy:.clusterid.fctr4 Hhold.fctrSKn:.clusterid.fctr4
## 0.393799216 0.006125892
## [1] TRUE
# From here to save(), this should all be in one function
# these are executed in the same seq twice more:
# fit.data.training & predict.data.new chunks
print(sprintf("%s fit prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet fit prediction diagnostics:"
glbObsFit <- glb_get_predictions(df = glbObsFit, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(sprintf("%s OOB prediction diagnostics:", glbMdlSelId))
## [1] "All.X##rcv#glmnet OOB prediction diagnostics:"
glbObsOOB <- glb_get_predictions(df = glbObsOOB, mdl_id = glbMdlSelId,
rsp_var = glb_rsp_var)
print(glb_featsimp_df <- myget_feats_importance(mdl = glb_sel_mdl, featsimp_df = NULL))
## All.X..rcv.glmnet.imp imp
## Hhold.fctrPKn 100.0000000 100.0000000
## Hhold.fctrMKy:.clusterid.fctr3 70.6541605 70.6541605
## Hhold.fctrSKn:.clusterid.fctr2 67.4604337 67.4604337
## Hhold.fctrPKy:.clusterid.fctr4 64.9623500 64.9623500
## Hhold.fctrMKn:.clusterid.fctr3 39.7725789 39.7725789
## Hhold.fctrPKn:.clusterid.fctr2 39.2939854 39.2939854
## Hhold.fctrMKy 37.4295156 37.4295156
## Hhold.fctrSKy 4.6944480 4.6944480
## Hhold.fctrSKn:.clusterid.fctr4 0.8619652 0.8619652
## .rnorm 0.0000000 0.0000000
## Hhold.fctrMKn 0.0000000 0.0000000
## Hhold.fctrPKy 0.0000000 0.0000000
## Hhold.fctrSKn 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000 0.0000000
#mdl_id <-"RFE.X.glmnet"; glb_featsimp_df <- myget_feats_importance(glb_models_lst[[mdl_id]], glb_featsimp_df); glb_featsimp_df[, paste0(mdl_id, ".imp")] <- glb_featsimp_df$imp; print(glb_featsimp_df)
#print(head(sbst_featsimp_df <- subset(glb_featsimp_df, is.na(RFE.X.glmnet.imp) | (abs(RFE.X.YeoJohnson.glmnet.imp - RFE.X.glmnet.imp) > 0.0001), select=-imp)))
#print(orderBy(~ -cor.y.abs, subset(glb_feats_df, id %in% c(row.names(sbst_featsimp_df), "startprice.dcm1.is9", "D.weight.post.stop.sum"))))
# Used again in fit.data.training & predict.data.new chunks
glb_analytics_diag_plots <- function(obs_df, mdl_id, prob_threshold=NULL) {
if (!is.null(featsimp_df <- glb_featsimp_df)) {
featsimp_df$feat <- gsub("`(.*?)`", "\\1", row.names(featsimp_df))
featsimp_df$feat.interact <- gsub("(.*?):(.*)", "\\2", featsimp_df$feat)
featsimp_df$feat <- gsub("(.*?):(.*)", "\\1", featsimp_df$feat)
featsimp_df$feat.interact <-
ifelse(featsimp_df$feat.interact == featsimp_df$feat,
NA, featsimp_df$feat.interact)
featsimp_df$feat <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat)
featsimp_df$feat.interact <-
gsub("(.*?)\\.fctr(.*)", "\\1\\.fctr", featsimp_df$feat.interact)
featsimp_df <- orderBy(~ -imp.max,
summaryBy(imp ~ feat + feat.interact, data=featsimp_df,
FUN=max))
#rex_str=":(.*)"; txt_vctr=tail(featsimp_df$feat); ret_lst <- regexec(rex_str, txt_vctr); ret_lst <- regmatches(txt_vctr, ret_lst); ret_vctr <- sapply(1:length(ret_lst), function(pos_ix) ifelse(length(ret_lst[[pos_ix]]) > 0, ret_lst[[pos_ix]], "")); print(ret_vctr <- ret_vctr[ret_vctr != ""])
featsimp_df <- subset(featsimp_df, !is.na(imp.max))
if (nrow(featsimp_df) > 5) {
warning("Limiting important feature scatter plots to 5 out of ",
nrow(featsimp_df))
featsimp_df <- head(featsimp_df, 5)
}
# if (!all(is.na(featsimp_df$feat.interact)))
# stop("not implemented yet")
rsp_var_out <- mygetPredictIds(glb_rsp_var, mdl_id)$value
for (var in featsimp_df$feat) {
plot_df <- melt(obs_df, id.vars = var,
measure.vars = c(glb_rsp_var, rsp_var_out))
print(myplot_scatter(plot_df, var, "value", colorcol_name = "variable",
facet_colcol_name = "variable", jitter = TRUE) +
guides(color = FALSE))
}
}
if (glb_is_regression) {
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No important features in glb_fin_mdl") else
print(myplot_prediction_regression(df=obs_df,
feat_x=ifelse(nrow(featsimp_df) > 1, featsimp_df$feat[2],
".rownames"),
feat_y=featsimp_df$feat[1],
rsp_var=glb_rsp_var, rsp_var_out=rsp_var_out,
id_vars=glbFeatsId)
# + facet_wrap(reformulate(featsimp_df$feat[2])) # if [1 or 2] is a factor
# + geom_point(aes_string(color="<col_name>.fctr")) # to color the plot
)
}
if (glb_is_classification) {
require(lazyeval)
if (is.null(featsimp_df) || (nrow(featsimp_df) == 0))
warning("No features in selected model are statistically important")
else print(myplot_prediction_classification(df = obs_df,
feat_x = ifelse(nrow(featsimp_df) > 1,
featsimp_df$feat[2], ".rownames"),
feat_y = featsimp_df$feat[1],
rsp_var = glb_rsp_var,
rsp_var_out = rsp_var_out,
id_vars = glbFeatsId,
prob_threshold = prob_threshold))
}
}
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
prob_threshold = glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df = glbObsOOB, mdl_id = glbMdlSelId)
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 1 D 0.469114
## 2 18 D 0.469114
## 3 54 D 0.469114
## 4 82 D 0.469114
## 5 103 D 0.469114
## 6 221 D 0.469114
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 R TRUE
## 4 R TRUE
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.530886 FALSE
## 2 0.530886 FALSE
## 3 0.530886 FALSE
## 4 0.530886 FALSE
## 5 0.530886 FALSE
## 6 0.530886 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate Party.fctr.All.X..rcv.glmnet.error
## 1 FALSE -0.280886
## 2 FALSE -0.280886
## 3 FALSE -0.280886
## 4 FALSE -0.280886
## 5 FALSE -0.280886
## 6 FALSE -0.280886
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 8 361 D 0.4691140
## 11 460 D 0.4691140
## 40 1759 D 0.4691140
## 373 2655 D 0.5232774
## 403 1321 D 0.5286893
## 593 497 D 0.7062498
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 8 R TRUE
## 11 R TRUE
## 40 R TRUE
## 373 R TRUE
## 403 R TRUE
## 593 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 8 0.5308860
## 11 0.5308860
## 40 0.5308860
## 373 0.4767226
## 403 0.4713107
## 593 0.2937502
## Party.fctr.All.X..rcv.glmnet.is.acc
## 8 FALSE
## 11 FALSE
## 40 FALSE
## 373 FALSE
## 403 FALSE
## 593 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 8 FALSE
## 11 FALSE
## 40 FALSE
## 373 FALSE
## 403 FALSE
## 593 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 8 -0.28088603
## 11 -0.28088603
## 40 -0.28088603
## 373 -0.22672265
## 403 -0.22131067
## 593 -0.04375019
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 589 4662 D 0.6580649
## 590 5410 D 0.6580649
## 591 6477 D 0.6580649
## 592 6885 D 0.6580649
## 593 497 D 0.7062498
## 594 1007 D 0.7062498
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 589 R TRUE
## 590 R TRUE
## 591 R TRUE
## 592 R TRUE
## 593 R TRUE
## 594 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 589 0.3419351
## 590 0.3419351
## 591 0.3419351
## 592 0.3419351
## 593 0.2937502
## 594 0.2937502
## Party.fctr.All.X..rcv.glmnet.is.acc
## 589 FALSE
## 590 FALSE
## 591 FALSE
## 592 FALSE
## 593 FALSE
## 594 FALSE
## Party.fctr.All.X..rcv.glmnet.accurate
## 589 FALSE
## 590 FALSE
## 591 FALSE
## 592 FALSE
## 593 FALSE
## 594 FALSE
## Party.fctr.All.X..rcv.glmnet.error
## 589 -0.09193505
## 590 -0.09193505
## 591 -0.09193505
## 592 -0.09193505
## 593 -0.04375019
## 594 -0.04375019
if (!is.null(glbFeatsCategory)) {
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsFit, mdl_id = glbMdlSelId,
label = "fit"),
by = glbFeatsCategory, all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
glbLvlCategory <- merge(glbLvlCategory,
myget_category_stats(obs_df = glbObsOOB, mdl_id = glbMdlSelId,
label="OOB"),
#by=glbFeatsCategory, all=TRUE) glb_ctgry-df already contains .n.OOB ?
all = TRUE)
row.names(glbLvlCategory) <- glbLvlCategory[, glbFeatsCategory]
if (any(grepl("OOB", glbMdlMetricsEval)))
print(orderBy(~-err.abs.OOB.mean, glbLvlCategory)) else
print(orderBy(~-err.abs.fit.mean, glbLvlCategory))
print(colSums(glbLvlCategory[, -grep(glbFeatsCategory, names(glbLvlCategory))]))
}
## Hhold.fctr .n.OOB .n.Fit .n.Tst .freqRatio.Fit .freqRatio.OOB
## N N 83 367 102 0.08250899 0.074107143
## SKy SKy 53 147 65 0.03304856 0.047321429
## MKn MKn 136 516 169 0.11600719 0.121428571
## SKn SKn 511 1920 638 0.43165468 0.456250000
## MKy MKy 298 1296 371 0.29136691 0.266071429
## PKn PKn 30 150 37 0.03372302 0.026785714
## PKy PKy 9 52 10 0.01169065 0.008035714
## .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean .n.fit err.abs.OOB.sum
## N 0.073275862 183.38970 0.4996995 367 41.38970
## SKy 0.046695402 72.49587 0.4931692 147 26.41393
## MKn 0.121408046 255.65579 0.4954570 516 67.71214
## SKn 0.458333333 938.03522 0.4885600 1920 251.54176
## MKy 0.266522989 638.59067 0.4927397 1296 146.66848
## PKn 0.026580460 60.57196 0.4038131 150 14.73205
## PKy 0.007183908 24.83285 0.4775549 52 4.38788
## err.abs.OOB.mean
## N 0.4986711
## SKy 0.4983761
## MKn 0.4978833
## SKn 0.4922539
## MKy 0.4921761
## PKn 0.4910685
## PKy 0.4875422
## .n.OOB .n.Fit .n.Tst .freqRatio.Fit
## 1120.000000 4448.000000 1392.000000 1.000000
## .freqRatio.OOB .freqRatio.Tst err.abs.fit.sum err.abs.fit.mean
## 1.000000 1.000000 2173.572078 3.350993
## .n.fit err.abs.OOB.sum err.abs.OOB.mean
## 4448.000000 552.845950 3.457971
write.csv(glbObsOOB[, c(glbFeatsId,
grep(glb_rsp_var, names(glbObsOOB), fixed=TRUE, value=TRUE))],
paste0(gsub(".", "_", paste0(glbOut$pfx, glbMdlSelId), fixed=TRUE),
"_OOBobs.csv"), row.names=FALSE)
fit.models_2_chunk_df <-
myadd_chunk(NULL, "fit.models_2_bgn", label.minor = "teardown")
## label step_major step_minor label_minor bgn end elapsed
## 1 fit.models_2_bgn 1 0 teardown 323.555 NA NA
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.models", major.inc=FALSE)
## label step_major step_minor label_minor bgn end elapsed
## 6 fit.models 4 2 2 312.890 323.566 10.676
## 7 fit.models 4 3 3 323.566 NA NA
# if (sum(is.na(glbObsAll$D.P.http)) > 0)
# stop("fit.models_3: Why is this happening ?")
#stop(here"); glb2Sav()
sync_glb_obs_df <- function() {
# Merge or cbind ?
for (col in setdiff(names(glbObsFit), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "Fit", col] <<- glbObsFit[, col]
for (col in setdiff(names(glbObsFit), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "Fit", col] <<- glbObsFit[, col]
if (all(is.na(glbObsNew[, glb_rsp_var])))
for (col in setdiff(names(glbObsOOB), names(glbObsTrn)))
glbObsTrn[glbObsTrn$.lcn == "OOB", col] <<- glbObsOOB[, col]
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
glbObsAll[glbObsAll$.lcn == "OOB", col] <<- glbObsOOB[, col]
}
sync_glb_obs_df()
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"model.selected")), flip_coord = TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc = TRUE)
## label step_major step_minor label_minor bgn end
## 7 fit.models 4 3 3 323.566 327.504
## 8 fit.data.training 5 0 0 327.504 NA
## elapsed
## 7 3.938
## 8 NA
5.0: fit data training#load(paste0(glb_inp_pfx, "dsk.RData"))
if (!is.null(glbMdlFinId) && (glbMdlFinId %in% names(glb_models_lst))) {
warning("Final model same as user selected model")
glb_fin_mdl <- glb_models_lst[[glbMdlFinId]]
} else
# if (nrow(glbObsFit) + length(glbObsFitOutliers) == nrow(glbObsTrn))
if (!all(is.na(glbObsNew[, glb_rsp_var])))
{
warning("Final model same as glbMdlSelId")
glbMdlFinId <- paste0("Final.", glbMdlSelId)
glb_fin_mdl <- glb_sel_mdl
glb_models_lst[[glbMdlFinId]] <- glb_fin_mdl
mdlDf <- glb_models_df[glb_models_df$id == glbMdlSelId, ]
mdlDf$id <- glbMdlFinId
glb_models_df <- rbind(glb_models_df, mdlDf)
} else {
if (grepl("RFE\\.X", names(glbMdlFamilies))) {
indepVar <- mygetIndepVar(glb_feats_df)
rfe_trn_results <-
myrun_rfe(glbObsTrn, indepVar, glbRFESizes[["Final"]])
if (!isTRUE(all.equal(sort(predictors(rfe_trn_results)),
sort(predictors(rfe_fit_results))))) {
print("Diffs predictors(rfe_trn_results) vs. predictors(rfe_fit_results):")
print(setdiff(predictors(rfe_trn_results), predictors(rfe_fit_results)))
print("Diffs predictors(rfe_fit_results) vs. predictors(rfe_trn_results):")
print(setdiff(predictors(rfe_fit_results), predictors(rfe_trn_results)))
}
}
# }
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
# Fit selected models on glbObsTrn
for (mdl_id in gsub(".prob", "",
gsub(mygetPredictIds(glb_rsp_var)$value, "", row.names(mdlimp_df), fixed = TRUE),
fixed = TRUE)) {
mdl_id_components <- unlist(strsplit(mdl_id, "[.]"))
mdlIdPfx <- paste0(c(head(mdl_id_components, -1), "Train"),
collapse = ".")
if (grepl("RFE\\.X\\.", mdlIdPfx))
mdlIndepVars <- myadjustInteractionFeats(glb_feats_df, myextract_actual_feats(
predictors(rfe_trn_results))) else
mdlIndepVars <- trim(unlist(
strsplit(glb_models_df[glb_models_df$id == mdl_id, "feats"], "[,]")))
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdlIdPfx,
type = glb_model_type, tune.df = glbMdlTuneParams,
trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = tail(mdl_id_components, 1))),
indepVar = mdlIndepVars,
rsp_var = glb_rsp_var,
fit_df = glbObsTrn, OOB_df = NULL)
glbObsTrn <- glb_get_predictions(df = glbObsTrn,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
glbObsNew <- glb_get_predictions(df = glbObsNew,
mdl_id = tail(glb_models_df$id, 1),
rsp_var = glb_rsp_var,
prob_threshold_def =
subset(glb_models_df, id == mdl_id)$opt.prob.threshold.OOB)
}
}
# "Final" model
if ((model_method <- glb_sel_mdl$method) == "custom")
# get actual method from the mdl_id
model_method <- tail(unlist(strsplit(glbMdlSelId, "[.]")), 1)
if (grepl("Ensemble", glbMdlSelId)) {
# Find which models are relevant
mdlimp_df <- subset(myget_feats_importance(glb_sel_mdl), imp > 5)
if (glb_is_classification && glb_is_binomial)
indepVar <- gsub("(.*)\\.(.*)\\.prob", "\\1\\.Train\\.\\2\\.prob",
row.names(mdlimp_df)) else
indepVar <- gsub("(.*)\\.(.*)", "\\1\\.Train\\.\\2",
row.names(mdlimp_df))
} else
if (grepl("RFE.X", glbMdlSelId, fixed = TRUE)) {
indepVar <- myextract_actual_feats(predictors(rfe_trn_results))
} else indepVar <-
trim(unlist(strsplit(glb_models_df[glb_models_df$id ==
glbMdlSelId
, "feats"], "[,]")))
if (!is.null(glb_preproc_methods) &&
((match_pos <- regexpr(gsub(".", "\\.",
paste(glb_preproc_methods, collapse = "|"),
fixed = TRUE), glbMdlSelId)) != -1))
ths_preProcess <- str_sub(glbMdlSelId, match_pos,
match_pos + attr(match_pos, "match.length") - 1) else
ths_preProcess <- NULL
mdl_id_pfx <- ifelse(grepl("Ensemble", glbMdlSelId),
"Final.Ensemble", "Final")
trnobs_df <- glbObsTrn
if (!is.null(glbObsTrnOutliers[[mdl_id_pfx]])) {
trnobs_df <- glbObsTrn[!(glbObsTrn[, glbFeatsId] %in% glbObsTrnOutliers[[mdl_id_pfx]]), ]
print(sprintf("Outliers removed: %d", nrow(glbObsTrn) - nrow(trnobs_df)))
print(setdiff(glbObsTrn[, glbFeatsId], trnobs_df[, glbFeatsId]))
}
# Force fitting of Final.glm to identify outliers
method_vctr <- unique(c(myparseMdlId(glbMdlSelId)$alg, glbMdlFamilies[["Final"]]))
for (method in method_vctr) {
#source("caret_nominalTrainWorkflow.R")
# glmnet requires at least 2 indep vars
if ((length(indepVar) == 1) && (method %in% "glmnet"))
next
ret_lst <-
myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst = list(
id.prefix = mdl_id_pfx,
type = glb_model_type, trainControl.method = "repeatedcv",
trainControl.number = glb_rcv_n_folds,
trainControl.repeats = glb_rcv_n_repeats,
trainControl.classProbs = glb_is_classification,
trainControl.summaryFunction = glbMdlMetricSummaryFn,
trainControl.allowParallel = glbMdlAllowParallel,
train.metric = glbMdlMetricSummary,
train.maximize = glbMdlMetricMaximize,
train.method = method,
train.preProcess = ths_preProcess)),
indepVar = indepVar, rsp_var = glb_rsp_var,
fit_df = trnobs_df, OOB_df = NULL)
if ((length(method_vctr) == 1) || (method != "glm")) {
glb_fin_mdl <- glb_models_lst[[length(glb_models_lst)]]
glbMdlFinId <- glb_models_df[length(glb_models_lst), "id"]
}
}
}
## [1] "myfit_mdl: enter: 0.000000 secs"
## [1] "fitting model: Final##rcv#glmnet"
## [1] " indepVar: Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr"
## [1] "myfit_mdl: setup complete: 0.709000 secs"
## Aggregating results
## Selecting tuning parameters
## Fitting alpha = 0.1, lambda = 0.000925 on full training set
## [1] "myfit_mdl: train complete: 4.478000 secs"
## Warning in myfit_mdl(mdl_specs_lst = myinit_mdl_specs_lst(mdl_specs_lst
## = list(id.prefix = mdl_id_pfx, : model's bestTune found at an extreme of
## tuneGrid for parameter: alpha
## Length Class Mode
## a0 58 -none- numeric
## beta 1624 dgCMatrix S4
## df 58 -none- numeric
## dim 2 -none- numeric
## lambda 58 -none- numeric
## dev.ratio 58 -none- numeric
## nulldev 1 -none- numeric
## npasses 1 -none- numeric
## jerr 1 -none- numeric
## offset 1 -none- logical
## classnames 2 -none- character
## call 5 -none- call
## nobs 1 -none- numeric
## lambdaOpt 1 -none- numeric
## xNames 28 -none- character
## problemType 1 -none- character
## tuneValue 2 data.frame list
## obsLevels 2 -none- character
## [1] "min lambda > lambdaOpt:"
## (Intercept) .rnorm
## 0.05674217 -0.01683326
## Hhold.fctrMKn Hhold.fctrMKy
## -0.09892081 -0.39327027
## Hhold.fctrPKn Hhold.fctrPKy
## 0.85896305 0.02841362
## Hhold.fctrSKn Hhold.fctrSKy
## -0.15728881 0.56548214
## Hhold.fctrN:.clusterid.fctr2 Hhold.fctrMKn:.clusterid.fctr2
## -0.06488410 0.20661936
## Hhold.fctrMKy:.clusterid.fctr2 Hhold.fctrPKn:.clusterid.fctr2
## 0.34547637 0.58504330
## Hhold.fctrPKy:.clusterid.fctr2 Hhold.fctrSKn:.clusterid.fctr2
## -0.28751286 0.70656256
## Hhold.fctrSKy:.clusterid.fctr2 Hhold.fctrN:.clusterid.fctr3
## -0.81611026 -0.05028136
## Hhold.fctrMKn:.clusterid.fctr3 Hhold.fctrMKy:.clusterid.fctr3
## 0.80626250 1.02572623
## Hhold.fctrPKn:.clusterid.fctr3 Hhold.fctrPKy:.clusterid.fctr3
## -0.61036216 -0.40346940
## Hhold.fctrSKn:.clusterid.fctr3 Hhold.fctrSKy:.clusterid.fctr3
## 0.27587858 -0.53956389
## Hhold.fctrPKy:.clusterid.fctr4 Hhold.fctrSKn:.clusterid.fctr4
## 2.35706143 0.39303011
## [1] "max lambda < lambdaOpt:"
## [1] "Feats mismatch between coefs_left & rght:"
## [1] "(Intercept)" ".rnorm"
## [3] "Hhold.fctrMKn" "Hhold.fctrMKy"
## [5] "Hhold.fctrPKn" "Hhold.fctrPKy"
## [7] "Hhold.fctrSKn" "Hhold.fctrSKy"
## [9] "Hhold.fctrN:.clusterid.fctr2" "Hhold.fctrMKn:.clusterid.fctr2"
## [11] "Hhold.fctrMKy:.clusterid.fctr2" "Hhold.fctrPKn:.clusterid.fctr2"
## [13] "Hhold.fctrPKy:.clusterid.fctr2" "Hhold.fctrSKn:.clusterid.fctr2"
## [15] "Hhold.fctrSKy:.clusterid.fctr2" "Hhold.fctrN:.clusterid.fctr3"
## [17] "Hhold.fctrMKn:.clusterid.fctr3" "Hhold.fctrMKy:.clusterid.fctr3"
## [19] "Hhold.fctrPKn:.clusterid.fctr3" "Hhold.fctrPKy:.clusterid.fctr3"
## [21] "Hhold.fctrSKn:.clusterid.fctr3" "Hhold.fctrSKy:.clusterid.fctr3"
## [23] "Hhold.fctrN:.clusterid.fctr4" "Hhold.fctrMKn:.clusterid.fctr4"
## [25] "Hhold.fctrMKy:.clusterid.fctr4" "Hhold.fctrPKn:.clusterid.fctr4"
## [27] "Hhold.fctrPKy:.clusterid.fctr4" "Hhold.fctrSKn:.clusterid.fctr4"
## [29] "Hhold.fctrSKy:.clusterid.fctr4"
## [1] "myfit_mdl: train diagnostics complete: 5.088000 secs"
## Prediction
## Reference R D
## R 2579 38
## D 2823 128
## Accuracy Kappa AccuracyLower AccuracyUpper AccuracyNull
## 0.48617098 0.02721565 0.47296322 0.49939324 0.52999282
## AccuracyPValue McnemarPValue
## 1.00000000 0.00000000
## [1] "myfit_mdl: predict complete: 9.197000 secs"
## id feats
## 1 Final##rcv#glmnet Hhold.fctr,.rnorm,Hhold.fctr:.clusterid.fctr
## max.nTuningRuns min.elapsedtime.everything min.elapsedtime.final
## 1 20 3.756 0.165
## max.AUCpROC.fit max.Sens.fit max.Spec.fit max.AUCROCR.fit
## 1 0.5741715 0.5651509 0.5831921 0.3939275
## opt.prob.threshold.fit max.f.score.fit max.Accuracy.fit
## 1 0.7 0.6432223 0.5631569
## max.AccuracyLower.fit max.AccuracyUpper.fit max.Kappa.fit
## 1 0.4729632 0.4993932 0.1234567
## max.AccuracySD.fit max.KappaSD.fit
## 1 0.007710589 0.016188
## [1] "myfit_mdl: exit: 9.214000 secs"
rm(ret_lst)
glb_chunks_df <- myadd_chunk(glb_chunks_df, "fit.data.training", major.inc=FALSE)
## label step_major step_minor label_minor bgn end
## 8 fit.data.training 5 0 0 327.504 337.342
## 9 fit.data.training 5 1 1 337.342 NA
## elapsed
## 8 9.838
## 9 NA
#stop(here"); glb2Sav()
if (glb_is_classification && glb_is_binomial)
prob_threshold <- glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"] else
prob_threshold <- NULL
if (grepl("Ensemble", glbMdlFinId)) {
# Get predictions for each model in ensemble; Outliers that have been moved to OOB might not have been predicted yet
mdlEnsembleComps <- unlist(str_split(subset(glb_models_df,
id == glbMdlFinId)$feats, ","))
if (glb_is_classification && glb_is_binomial)
mdlEnsembleComps <- gsub("\\.prob$", "", mdlEnsembleComps)
mdlEnsembleComps <- gsub(paste0("^",
gsub(".", "\\.", mygetPredictIds(glb_rsp_var)$value, fixed = TRUE)),
"", mdlEnsembleComps)
for (mdl_id in mdlEnsembleComps) {
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
glbObsNew <- glb_get_predictions(df = glbObsNew, mdl_id = mdl_id,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
}
}
glbObsTrn <- glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
rsp_var = glb_rsp_var,
prob_threshold_def = prob_threshold)
## Warning in glb_get_predictions(df = glbObsTrn, mdl_id = glbMdlFinId,
## rsp_var = glb_rsp_var, : Using default probability threshold: 0.75
glb_featsimp_df <- myget_feats_importance(mdl=glb_fin_mdl,
featsimp_df=glb_featsimp_df)
#glb_featsimp_df[, paste0(glbMdlFinId, ".imp")] <- glb_featsimp_df$imp
print(glb_featsimp_df)
## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Hhold.fctrPKy:.clusterid.fctr4 64.9623500 100.0000000
## Hhold.fctrMKy:.clusterid.fctr3 70.6541605 43.5171616
## Hhold.fctrPKn 100.0000000 36.4421157
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 34.6240557
## Hhold.fctrMKn:.clusterid.fctr3 39.7725789 34.2062574
## Hhold.fctrSKn:.clusterid.fctr2 67.4604337 29.9764167
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 25.8950467
## Hhold.fctrPKn:.clusterid.fctr2 39.2939854 24.8208763
## Hhold.fctrSKy 4.6944480 23.9909801
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000 22.8913802
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 17.1174749
## Hhold.fctrMKy 37.4295156 16.6847698
## Hhold.fctrSKn:.clusterid.fctr4 0.8619652 16.6745807
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 14.6570798
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 12.1979366
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 11.7043442
## Hhold.fctrMKn:.clusterid.fctr2 0.0000000 8.7659727
## Hhold.fctrSKn 0.0000000 6.6730893
## Hhold.fctrMKn 0.0000000 4.1967855
## Hhold.fctrN:.clusterid.fctr2 0.0000000 2.7527540
## Hhold.fctrN:.clusterid.fctr3 0.0000000 2.1332222
## Hhold.fctrPKy 0.0000000 1.2054678
## .rnorm 0.0000000 0.7141628
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000 0.0000000
## imp
## Hhold.fctrPKy:.clusterid.fctr4 100.0000000
## Hhold.fctrMKy:.clusterid.fctr3 43.5171616
## Hhold.fctrPKn 36.4421157
## Hhold.fctrSKy:.clusterid.fctr2 34.6240557
## Hhold.fctrMKn:.clusterid.fctr3 34.2062574
## Hhold.fctrSKn:.clusterid.fctr2 29.9764167
## Hhold.fctrPKn:.clusterid.fctr3 25.8950467
## Hhold.fctrPKn:.clusterid.fctr2 24.8208763
## Hhold.fctrSKy 23.9909801
## Hhold.fctrSKy:.clusterid.fctr3 22.8913802
## Hhold.fctrPKy:.clusterid.fctr3 17.1174749
## Hhold.fctrMKy 16.6847698
## Hhold.fctrSKn:.clusterid.fctr4 16.6745807
## Hhold.fctrMKy:.clusterid.fctr2 14.6570798
## Hhold.fctrPKy:.clusterid.fctr2 12.1979366
## Hhold.fctrSKn:.clusterid.fctr3 11.7043442
## Hhold.fctrMKn:.clusterid.fctr2 8.7659727
## Hhold.fctrSKn 6.6730893
## Hhold.fctrMKn 4.1967855
## Hhold.fctrN:.clusterid.fctr2 2.7527540
## Hhold.fctrN:.clusterid.fctr3 2.1332222
## Hhold.fctrPKy 1.2054678
## .rnorm 0.7141628
## Hhold.fctrMKn:.clusterid.fctr4 0.0000000
## Hhold.fctrMKy:.clusterid.fctr4 0.0000000
## Hhold.fctrN:.clusterid.fctr4 0.0000000
## Hhold.fctrPKn:.clusterid.fctr4 0.0000000
## Hhold.fctrSKy:.clusterid.fctr4 0.0000000
if (glb_is_classification && glb_is_binomial)
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId,
prob_threshold=glb_models_df[glb_models_df$id == glbMdlSelId,
"opt.prob.threshold.OOB"]) else
glb_analytics_diag_plots(obs_df=glbObsTrn, mdl_id=glbMdlFinId)
## [1] "Min/Max Boundaries: "
## [1] "Inaccurate: "
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 1 4314 D 0.469114
## 2 662 D 0.469114
## 3 3381 D NA
## 4 2311 D NA
## 5 3406 D 0.469114
## 6 4373 D 0.469114
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 1 R TRUE
## 2 R TRUE
## 3 <NA> NA
## 4 <NA> NA
## 5 R TRUE
## 6 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs Party.fctr.All.X..rcv.glmnet.is.acc
## 1 0.530886 FALSE
## 2 0.530886 FALSE
## 3 NA NA
## 4 NA NA
## 5 0.530886 FALSE
## 6 0.530886 FALSE
## Party.fctr.Final..rcv.glmnet.prob Party.fctr.Final..rcv.glmnet
## 1 0.4042977 R
## 2 0.4045484 R
## 3 0.4054978 R
## 4 0.4059905 R
## 5 0.4060469 R
## 6 0.4068453 R
## Party.fctr.Final..rcv.glmnet.err Party.fctr.Final..rcv.glmnet.err.abs
## 1 TRUE 0.5957023
## 2 TRUE 0.5954516
## 3 TRUE 0.5945022
## 4 TRUE 0.5940095
## 5 TRUE 0.5939531
## 6 TRUE 0.5931547
## Party.fctr.Final..rcv.glmnet.is.acc
## 1 FALSE
## 2 FALSE
## 3 FALSE
## 4 FALSE
## 5 FALSE
## 6 FALSE
## Party.fctr.Final..rcv.glmnet.accurate Party.fctr.Final..rcv.glmnet.error
## 1 FALSE -0.3457023
## 2 FALSE -0.3454516
## 3 FALSE -0.3445022
## 4 FALSE -0.3440095
## 5 FALSE -0.3439531
## 6 FALSE -0.3431547
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 12 3649 D 0.4691140
## 196 476 D 0.4691140
## 790 2908 D 0.5220593
## 918 3204 D 0.5220593
## 1593 6177 D 0.5220593
## 2781 3610 D 0.5777544
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 12 R TRUE
## 196 R TRUE
## 790 R TRUE
## 918 R TRUE
## 1593 R TRUE
## 2781 R TRUE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 12 0.5308860
## 196 0.5308860
## 790 0.4779407
## 918 0.4779407
## 1593 0.4779407
## 2781 0.4222456
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 12 FALSE 0.4082715
## 196 FALSE 0.4162178
## 790 FALSE 0.4772844
## 918 FALSE 0.4828065
## 1593 FALSE 0.5400926
## 2781 FALSE 0.6791571
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 12 R TRUE
## 196 R TRUE
## 790 R TRUE
## 918 R TRUE
## 1593 R TRUE
## 2781 R TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 12 0.5917285
## 196 0.5837822
## 790 0.5227156
## 918 0.5171935
## 1593 0.4599074
## 2781 0.3208429
## Party.fctr.Final..rcv.glmnet.is.acc
## 12 FALSE
## 196 FALSE
## 790 FALSE
## 918 FALSE
## 1593 FALSE
## 2781 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 12 FALSE
## 196 FALSE
## 790 FALSE
## 918 FALSE
## 1593 FALSE
## 2781 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 12 -0.34172854
## 196 -0.33378216
## 790 -0.27271564
## 918 -0.26719348
## 1593 -0.20990745
## 2781 -0.07084288
## USER_ID Party.fctr Party.fctr.All.X..rcv.glmnet.prob
## 2898 3895 R 0.7062498
## 2899 3288 R 0.7062498
## 2900 2698 R NA
## 2901 1236 R 0.7062498
## 2902 1610 R 0.7062498
## 2903 626 R 0.6121203
## Party.fctr.All.X..rcv.glmnet Party.fctr.All.X..rcv.glmnet.err
## 2898 R FALSE
## 2899 R FALSE
## 2900 <NA> NA
## 2901 R FALSE
## 2902 R FALSE
## 2903 R FALSE
## Party.fctr.All.X..rcv.glmnet.err.abs
## 2898 0.7062498
## 2899 0.7062498
## 2900 NA
## 2901 0.7062498
## 2902 0.7062498
## 2903 0.6121203
## Party.fctr.All.X..rcv.glmnet.is.acc Party.fctr.Final..rcv.glmnet.prob
## 2898 TRUE 0.8195896
## 2899 TRUE 0.8209868
## 2900 NA 0.8210804
## 2901 TRUE 0.8212427
## 2902 TRUE 0.8230768
## 2903 TRUE 0.9190803
## Party.fctr.Final..rcv.glmnet Party.fctr.Final..rcv.glmnet.err
## 2898 D TRUE
## 2899 D TRUE
## 2900 D TRUE
## 2901 D TRUE
## 2902 D TRUE
## 2903 D TRUE
## Party.fctr.Final..rcv.glmnet.err.abs
## 2898 0.8195896
## 2899 0.8209868
## 2900 0.8210804
## 2901 0.8212427
## 2902 0.8230768
## 2903 0.9190803
## Party.fctr.Final..rcv.glmnet.is.acc
## 2898 FALSE
## 2899 FALSE
## 2900 FALSE
## 2901 FALSE
## 2902 FALSE
## 2903 FALSE
## Party.fctr.Final..rcv.glmnet.accurate
## 2898 FALSE
## 2899 FALSE
## 2900 FALSE
## 2901 FALSE
## 2902 FALSE
## 2903 FALSE
## Party.fctr.Final..rcv.glmnet.error
## 2898 0.06958955
## 2899 0.07098684
## 2900 0.07108042
## 2901 0.07124266
## 2902 0.07307676
## 2903 0.16908028
dsp_feats_vctr <- c(NULL)
for(var in grep(".imp", names(glb_feats_df), fixed=TRUE, value=TRUE))
dsp_feats_vctr <- union(dsp_feats_vctr,
glb_feats_df[!is.na(glb_feats_df[, var]), "id"])
# print(glbObsTrn[glbObsTrn$UniqueID %in% FN_OOB_ids,
# grep(glb_rsp_var, names(glbObsTrn), value=TRUE)])
print(setdiff(names(glbObsTrn), names(glbObsAll)))
## [1] "Party.fctr.Final..rcv.glmnet.prob"
## [2] "Party.fctr.Final..rcv.glmnet"
## [3] "Party.fctr.Final..rcv.glmnet.err"
## [4] "Party.fctr.Final..rcv.glmnet.err.abs"
## [5] "Party.fctr.Final..rcv.glmnet.is.acc"
for (col in setdiff(names(glbObsTrn), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.src == "Train", col] <- glbObsTrn[, col]
print(setdiff(names(glbObsFit), names(glbObsAll)))
## character(0)
print(setdiff(names(glbObsOOB), names(glbObsAll)))
## character(0)
for (col in setdiff(names(glbObsOOB), names(glbObsAll)))
# Merge or cbind ?
glbObsAll[glbObsAll$.lcn == "OOB", col] <- glbObsOOB[, col]
print(setdiff(names(glbObsNew), names(glbObsAll)))
## character(0)
#glb2Sav(); all.equal(savObsAll, glbObsAll); all.equal(sav_models_lst, glb_models_lst)
#load(file = paste0(glbOut$pfx, "dsk_knitr.RData"))
#cmpCols <- names(glbObsAll)[!grepl("\\.Final\\.", names(glbObsAll))]; all.equal(savObsAll[, cmpCols], glbObsAll[, cmpCols]); all.equal(savObsAll[, "H.P.http"], glbObsAll[, "H.P.http"]);
replay.petrisim(pn = glb_analytics_pn,
replay.trans = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs,
"data.training.all.prediction","model.final")), flip_coord = TRUE)
## time trans "bgn " "fit.data.training.all " "predict.data.new " "end "
## 0.0000 multiple enabled transitions: data.training.all data.new model.selected firing: model.selected
## 1.0000 3 2 1 0 0
## 1.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: data.training.all.prediction
## 2.0000 5 2 0 0 1
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans =
## (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Transition:
## model.final not enabled; adding missing token(s)
## Warning in replay.petrisim(pn = glb_analytics_pn, replay.trans
## = (glb_analytics_avl_objs <- c(glb_analytics_avl_objs, : Place:
## fit.data.training.all: added 1 missing token
## 2.0000 multiple enabled transitions: data.training.all data.new model.selected model.final data.training.all.prediction firing: model.final
## 3.0000 4 2 0 1 1
glb_chunks_df <- myadd_chunk(glb_chunks_df, "predict.data.new", major.inc = TRUE)
## label step_major step_minor label_minor bgn end
## 9 fit.data.training 5 1 1 337.342 346.936
## 10 predict.data.new 6 0 0 346.937 NA
## elapsed
## 9 9.594
## 10 NA
6.0: predict data new## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.75
## Warning in glb_get_predictions(obs_df, mdl_id = glbMdlFinId, rsp_var =
## glb_rsp_var, : Using default probability threshold: 0.75
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## Warning: Removed 1392 rows containing missing values (geom_point).
## NULL
## [1] "OOBobs total range outliers: 0"
## [1] "newobs total range outliers: 0"
## [1] 0.75
## [1] "glbMdlSelId: All.X##rcv#glmnet"
## [1] "glbMdlFinId: Final##rcv#glmnet"
## [1] "Cross Validation issues:"
## MFO###myMFO_classfr Random###myrandom_classfr
## 0 0
## Max.cor.Y.rcv.1X1###glmnet
## 0
## max.Accuracy.OOB max.AUCROCR.OOB
## All.X##rcv#glm 0.4705357 0.4324359
## Random###myrandom_classfr 0.4696429 0.5191202
## MFO###myMFO_classfr 0.4696429 0.5000000
## Max.cor.Y.rcv.1X1###glmnet 0.4696429 0.4525163
## Max.cor.Y##rcv#rpart 0.4696429 0.4470465
## Low.cor.X##rcv#glmnet 0.4696429 0.4464208
## All.X##rcv#glmnet 0.4696429 0.4464208
## Final##rcv#glmnet NA NA
## max.AUCpROC.OOB max.Accuracy.fit
## All.X##rcv#glm 0.5576807 0.5647478
## Random###myrandom_classfr 0.5235690 0.4700989
## MFO###myMFO_classfr 0.5000000 0.4700989
## Max.cor.Y.rcv.1X1###glmnet 0.5546626 0.4880845
## Max.cor.Y##rcv#rpart 0.5502714 0.5696192
## Low.cor.X##rcv#glmnet 0.5229705 0.5678975
## All.X##rcv#glmnet 0.5229705 0.5678975
## Final##rcv#glmnet NA 0.5631569
## opt.prob.threshold.fit opt.prob.threshold.OOB
## All.X##rcv#glm 0.70 0.90
## Random###myrandom_classfr 0.55 0.55
## MFO###myMFO_classfr 0.50 0.50
## Max.cor.Y.rcv.1X1###glmnet 0.70 0.85
## Max.cor.Y##rcv#rpart 0.70 0.80
## Low.cor.X##rcv#glmnet 0.65 0.75
## All.X##rcv#glmnet 0.65 0.75
## Final##rcv#glmnet 0.70 NA
## [1] "All.X##rcv#glmnet OOB confusion matrix & accuracy: "
## Prediction
## Reference R D
## R 526 0
## D 594 0
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## N 183.38970 41.38970 224.93015 NA
## SKy 72.49587 26.41393 93.67543 NA
## MKn 255.65579 67.71214 319.23871 NA
## SKn 938.03522 251.54176 1176.17051 NA
## MKy 638.59067 146.66848 767.08090 NA
## PKn 60.57196 14.73205 69.30674 NA
## PKy 24.83285 4.38788 25.16142 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit .n.New.D .n.New.R
## N 0.08250899 0.074107143 0.073275862 367 NA 102
## SKy 0.03304856 0.047321429 0.046695402 147 NA 65
## MKn 0.11600719 0.121428571 0.121408046 516 NA 169
## SKn 0.43165468 0.456250000 0.458333333 1920 NA 638
## MKy 0.29136691 0.266071429 0.266522989 1296 NA 371
## PKn 0.03372302 0.026785714 0.026580460 150 12 25
## PKy 0.01169065 0.008035714 0.007183908 52 1 9
## .n.OOB .n.Trn.D .n.Trn.R .n.Tst .n.fit .n.new .n.trn err.abs.OOB.mean
## N 83 230 220 102 367 102 450 0.4986711
## SKy 53 119 81 65 147 65 200 0.4983761
## MKn 136 344 308 169 516 169 652 0.4978833
## SKn 511 1340 1091 638 1920 638 2431 0.4922539
## MKy 298 752 842 371 1296 371 1594 0.4921761
## PKn 30 131 49 37 150 37 180 0.4910685
## PKy 9 35 26 10 52 10 61 0.4875422
## err.abs.fit.mean err.abs.new.mean err.abs.trn.mean
## N 0.4996995 NA 0.4998448
## SKy 0.4931692 NA 0.4683772
## MKn 0.4954570 NA 0.4896299
## SKn 0.4885600 NA 0.4838217
## MKy 0.4927397 NA 0.4812302
## PKn 0.4038131 NA 0.3850375
## PKy 0.4775549 NA 0.4124823
## err.abs.fit.sum err.abs.OOB.sum err.abs.trn.sum err.abs.new.sum
## 2173.572078 552.845950 2675.563871 NA
## .freqRatio.Fit .freqRatio.OOB .freqRatio.Tst .n.Fit
## 1.000000 1.000000 1.000000 4448.000000
## .n.New.D .n.New.R .n.OOB .n.Trn.D
## NA 1379.000000 1120.000000 2951.000000
## .n.Trn.R .n.Tst .n.fit .n.new
## 2617.000000 1392.000000 4448.000000 1392.000000
## .n.trn err.abs.OOB.mean err.abs.fit.mean err.abs.new.mean
## 5568.000000 3.457971 3.350993 NA
## err.abs.trn.mean
## 3.220424
## [1] "Features Importance for selected models:"
## All.X..rcv.glmnet.imp Final..rcv.glmnet.imp
## Hhold.fctrPKn 100.0000000 36.44212
## Hhold.fctrMKy:.clusterid.fctr3 70.6541605 43.51716
## Hhold.fctrSKn:.clusterid.fctr2 67.4604337 29.97642
## Hhold.fctrPKy:.clusterid.fctr4 64.9623500 100.00000
## Hhold.fctrMKn:.clusterid.fctr3 39.7725789 34.20626
## Hhold.fctrPKn:.clusterid.fctr2 39.2939854 24.82088
## Hhold.fctrMKy 37.4295156 16.68477
## Hhold.fctrSKy 4.6944480 23.99098
## Hhold.fctrSKn:.clusterid.fctr4 0.8619652 16.67458
## Hhold.fctrSKy:.clusterid.fctr2 0.0000000 34.62406
## Hhold.fctrPKn:.clusterid.fctr3 0.0000000 25.89505
## Hhold.fctrSKy:.clusterid.fctr3 0.0000000 22.89138
## Hhold.fctrPKy:.clusterid.fctr3 0.0000000 17.11747
## Hhold.fctrMKy:.clusterid.fctr2 0.0000000 14.65708
## Hhold.fctrPKy:.clusterid.fctr2 0.0000000 12.19794
## Hhold.fctrSKn:.clusterid.fctr3 0.0000000 11.70434
## [1] "glbObsNew prediction stats:"
##
## R D
## 1379 13
## label step_major step_minor label_minor bgn end
## 10 predict.data.new 6 0 0 346.937 360.568
## 11 display.session.info 7 0 0 360.568 NA
## elapsed
## 10 13.631
## 11 NA
Null Hypothesis (\(\sf{H_{0}}\)): mpg is not impacted by am_fctr.
The variance by am_fctr appears to be independent. #{r q1, cache=FALSE} # print(t.test(subset(cars_df, am_fctr == "automatic")$mpg, # subset(cars_df, am_fctr == "manual")$mpg, # var.equal=FALSE)$conf) # We reject the null hypothesis i.e. we have evidence to conclude that am_fctr impacts mpg (95% confidence). Manual transmission is better for miles per gallon versus automatic transmission.
## label step_major step_minor label_minor bgn
## 1 cluster.data 1 0 0 6.109
## 2 partition.data.training 2 0 0 129.165
## 4 fit.models 4 0 0 245.736
## 5 fit.models 4 1 1 282.174
## 10 predict.data.new 6 0 0 346.937
## 6 fit.models 4 2 2 312.890
## 8 fit.data.training 5 0 0 327.504
## 9 fit.data.training 5 1 1 337.342
## 7 fit.models 4 3 3 323.566
## 3 select.features 3 0 0 242.864
## end elapsed duration
## 1 129.164 123.055 123.055
## 2 242.864 113.699 113.699
## 4 282.173 36.437 36.437
## 5 312.889 30.716 30.715
## 10 360.568 13.631 13.631
## 6 323.566 10.676 10.676
## 8 337.342 9.838 9.838
## 9 346.936 9.594 9.594
## 7 327.504 3.938 3.938
## 3 245.735 2.871 2.871
## [1] "Total Elapsed Time: 360.568 secs"